This week on Experts on Experts, I sat down with Sabeen Malik, Rapid7’s VP of Global Government Affairs and Public Policy, to discuss a shift security leaders can’t afford to treat as separate threads: frontier AI, vulnerability discovery, cybersecurity compliance, and operational resilience.

AI is changing how quickly vulnerabilities can be found, validated, and potentially exploited. At the same time, regulators, boards, and customers are asking for stronger proof that controls are working and risk is being reduced. Security leaders are being pushed to move at machine speed while proving the business is resilient.

AI vulnerability discovery is moving faster than security standards

Sabeen and I started with the policy question. Many of the systems security teams rely on today were designed for a slower era of human-led discovery. Vulnerability disclosure processes, scoring systems, prioritization frameworks, and regulatory expectations all assume organizations have time to assess, verify, and respond.

Frontier AI challenges that assumption. If models can help find and chain vulnerabilities faster, the industry needs stronger standards around verification, access, disclosure, and accountability. Access to powerful models matters, but access alone does not solve the governance problem. The bigger question is whether the ecosystem can responsibly validate, prioritize, and act on what these systems produce.

AI in cybersecurity must move from discovery to risk reduction

For defenders, faster discovery is only useful if it leads to faster action. Finding more vulnerabilities does not automatically make organizations safer. In many cases, it creates more noise for teams already under pressure.

The real challenge is exploitability. Security teams need to understand which risks are actually reachable, which issues matter most in their environment, and where action will reduce exposure fastest. That is where the shift from reactive security to preemptive security becomes critical. The goal is to use data, context, AI, and expertise to act earlier, not simply respond faster after something happens.

Cybersecurity compliance is becoming continuous

We also discussed how the compliance environment is changing. Organizations are no longer being asked to prove readiness once a year. Increasingly, they need to provide detailed evidence on shorter timelines across a growing set of regulatory and assurance requirements.

That creates a real challenge when evidence is collected manually or disconnected from live security operations. Leaders need to show what changed, what was fixed, who owns the response, and what risk remains. Static snapshots are no longer enough.

Cyber GRC connects security operations, risk, and compliance

One of the clearest themes from the conversation is that the future of security operations will be AI-driven, but human-led. AI can help teams move faster, surface what matters, and respond with greater scale and consistency. But governance, accountability, and judgment still matter.

That same principle applies to compliance. Security and compliance teams need live operational context, not disconnected reports. They need to connect what they detect, what they fix, and what they can prove.

Watch the full episode to hear our conversation on what this moment means for AI in cybersecurity, cybersecurity compliance, and resilient security operations:

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Introduction

The underground market for criminally oriented generative AI has moved beyond the early hype surrounding 'malicious chatbots.' The gradual integration of AI as a productivity layer within cybercrime operations has become the dominant story, indicating that while the potential for fully autonomous AI hacking systems is possible, attackers are not embracing them as expected. Instead, threat actors are increasingly using AI to accelerate routine, but operationally significant, tasks to scale their operations. Drafting phishing lures, profiling targets, debugging code, generating forged documents, modifying malware, translating victim communications, and processing stolen data at scale were once time-consuming activities that AI has made significantly easier. AI does not replace cybercriminals; it lowers friction, increases speed, and expands the range of actors able to perform tasks that previously required more time, skill, or external support.

AI is being absorbed into criminal tradecraft, embedding itself in social engineering, fraud enablement, impersonation, identity abuse, and post-breach data exploitation. The market supporting this demand is not a single coherent product category, but a broader ecosystem of jailbreak wrappers, Telegram-based bots, prompt packs, open-weight model deployments, stolen AI accounts, and hijacked API keys. Their importance lies less in technical elegance than in usability. They provide criminals with accessible, repeatable, and commercially packaged ways to apply AI to operational problems.

This ecosystem should not be mistaken for a stable or fully mature criminal market. Compared with more established sectors, criminal AI remains volatile, uneven, and heavily exposed to hype. Some services offer genuine operational utility while others are little more than repackaged public models marketed at inflated prices. Many are short-lived, deceptive, or opportunistic rebrands. 

Even so, the demand is real. The core shift is not the arrival of a single dominant criminal model, but the commercialization of access to AI-enabled criminal capability. The strategic significance of criminal AI lies in compressing time, lowering skill barriers, improving communication quality, and scaling existing criminal workflows.

Criminal AI-as-a-Service

The defining features of this market have little to do with any technical novelty, but rather the packaging and monetization of access. By early 2026, many underground services were marketed through familiar commercial mechanisms like subscriptions, private support channels, Telegram-based delivery, gated communities, and promises of uncensored output, privacy, or reduced logging. These are clear signs of SaaS-style commercialization, albeit far less mature or stable than its legitimate counterparts.

The market should be best understood as “Criminal AI-as-a-Service.” Most offerings do not appear to rely on original foundational models built by threat actors. Instead, they typically depend on jailbreaks, wrappers around commercial services, fine-tuned open-weight models, repackaged interfaces, or modular combinations of existing capabilities. 

Pricing patterns suggest growing commercialization, but not a stable market structure. Entry-level access may be inexpensive, while premium services can be marketed at significantly higher rates with promises of priority support or additional functionality. These prices should be treated as indicative, not definitive (Figures 1 and 2). They are highly volatile and shaped by takedowns, fraud, rebranding, and shifting demand. 

At the lower end, free tools and stolen access to legitimate AI services often remain the default. In the middle of the market, recurring subscriptions are increasingly common. At the upper end, some services claim to use more modular or self-hosted architectures to reduce dependence on mainstream platforms. Together, these patterns point to a market that is becoming more operationalized, even if it remains unstable and hype-driven.

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Main criminal AI tool families

The criminal AI ecosystem is defined by several distinct tool families that reflect how threat actors adopt, package, and market generative AI for illicit use. Some platforms function as fraud-enabling assistants, others as uncensored Telegram-native chatbots, modular offensive frameworks, or low-barrier tools aimed at novice users. Examining these categories is more useful than focusing solely on individual brand names, as it reveals the market’s underlying operational logic. That logic is based on how these tools are distributed, which users they target, and which stages of the criminal workflow they are designed to support. 

Overall, the market is increasingly splitting into two complementary directions. At one end are low-cost, mass-market tools that help less experienced actors produce phishing content, scam scripts, malware prompts, forged material, and social engineering narratives at scale. At the other end are more specialized platforms that integrate AI into execution workflows, supporting targeting, automation, and operational optimization for fewer but more precise attacks. This volume-versus-precision dynamic shows that criminal AI is no longer only about accelerating malicious content generation; it is also becoming a way to make illicit operations more scalable, quieter, and strategically targeted.

FraudGPT 

This tool family represents the distribution model for criminal AI by fraud shops. Emerging in mid-2023 for a few hundred dollars per month, its longevity on the black market stems from its positioning as an "all-in-one" operational assistant rather than a simple programming tool. Most buyers are not using it to engineer highly complex malware; instead, they treat it as a productivity engine to orchestrate the entire fraud chain. 

Threat actors use it to systematically design lookalike phishing pages, scrape target data, draft convincing spear-phishing lures, and generate scam scripts. Even as the underlying architecture has evolved away from standalone models and toward basic wrappers around legitimate, jailbroken corporate APIs, FraudGPT remains a staple of the underground economy because it effectively democratizes advanced social engineering, allowing entry-level scammers to execute highly localized, grammatically flawless, and high-volume fraud operations (Figure 3).

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GhostGPT 

This tool family reflects the Telegram-native distribution model. Its reported selling points — uncensored output, ease of access, and reduced operational friction — illustrate the convenience and perceived safety many criminal buyers claim to value most. However, like many tools in this category, independent verification of its capabilities is limited, and its significance lies more in what it signals about buyer preferences than in any confirmed technical differentiation.

WormGPT

This tool family serves as the ultimate case study in the power and persistence of criminal branding. While the original, headline-grabbing tool was officially shut down by its creator in August 2023 following intense law enforcement and media exposure, the name has essentially become a generic dark-web trademark for unrestricted AI. The market is saturated with opportunistic copycats, such as "WormGPT v4" and various Telegram bots trading on the name. 

Threat intelligence analysis of these modern variants reveals that they share zero code with the original system; instead, they are highly volatile marketing shells, often basic API wrappers around commercial models like Grok or Mixtral that use specialized system prompts to bypass safety guardrails. WormGPT's relevance in 2026 lies not in its technical uniqueness but in its sociological impact. It is an entry-level gateway tool used by script kiddies and sophisticated actors alike to quickly generate functional exploit scripts, craft persuasive business email compromise (BEC) lures, and scale offensive workflows (Figure 4).

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KawaiiGPT 

This is a freely accessible or low-cost criminally oriented AI chatbot/tool marketed in underground spaces to generate or support illicit content and cybercrime-related tasks. Its use highlights the problem of low-barrier access in the criminal LLM market. Its relevance does not lie in any demonstrated advanced capability and there is little evidence that it provides meaningful technical sophistication beyond basic generative AI functions. Rather, KawaiiGPT is important as an example of how free or near-free tools can normalize AI-assisted offending among less experienced users. Its significance is therefore sociological rather than technical as it lowers the threshold for participation, makes AI-assisted offending appear accessible and low-risk, and introduces novice actors to workflows such as phishing text generation, fraud scripting, impersonation, and other forms of low-level cybercrime support.

BruteForceAI 

This tool family represents a meaningfully different category from the chatbot-style tools that dominate criminal AI branding. BruteForceAI prioritizes precision over content generation. It integrates large language models for intelligent form analysis and sophisticated multi-threaded attack execution. This distinction matters. The broader trend it reflects is one of attackers making fewer, better-targeted attempts rather than relying on brute volume. AI here is not a content tool. It is an execution layer, and the shift from noisy credential stuffing to quiet, optimized targeting is strategically more significant than any individual tool name (Figure 5).

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Xanthorox 

This AI represents the modular criminal AI platform. Its significance lies in how it is marketed. Public reporting describes it as more than another “evil chatbot,” with claims around coding support, multiple model components, and broader operational utility. Still, Xanthorox should be framed cautiously. It is better treated as an emerging or ambitiously marketed platform than as a universally verified flagship of the underground market (Figure 6).

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The wide variety of smaller adversarial AI tools in 2026, including names like DarkGPT, EscapeGPT, WolfGPT, Evil-GPT, XXXGPT, and BadGPT, should be viewed with caution. These brands do not constitute a coherent or reliable category; instead, they often function as short-lived rebrandings or simple interfaces built on public or open-source models. In many cases, these are "scam-of-the-month" services hosted on Telegram, designed to capitalize on hype, with entry-level memberships starting at a few dozen dollars. However, they should not be dismissed outright, as some do offer genuine un-censorship or serve as testing grounds for malicious exploits. The bottom line in 2026 is that the brand name matters less than the underlying architecture. Most "GPT" labels are disposable marketing shells used to evade takedown measures or rebuild credibility after a service failure.

What truly defines the threat is the infrastructure supporting them. While entry-level tiers cost very little, professional-grade systems can cost thousands of dollars. At this level, the value isn't in the name, but in the technical setup.: These include the specific model used, how the service is delivered, the reliability of the operator, and how well it connects with other criminal tools like phishing kits, stealers, and ransomware support. Ultimately, the market has shifted toward operationalizing AI, focusing on tools that can automate and maximize the efficiency of entire illicit workflows.

Stolen AI accounts as an overlooked criminal market

One of the most important and still underappreciated developments in this landscape is the resale and abuse of legitimate AI access. This pattern is not new. Every widely adopted and commercially valuable technology eventually generates a secondary criminal market around stolen credentials, compromised accounts, and unauthorized access. AI is now following the same trajectory. Threat actors do not rely only on underground “dark AI” tools. They also misuse mainstream AI platforms directly.

However, the abuse of stolen AI accounts and hijacked API keys may be more consequential than many earlier credential markets. Access to legitimate AI services can provide threat actors with scalable cognitive and operational capabilities, not just access to a single platform or dataset. A compromised AI account may enable faster reconnaissance, multilingual targeting, automated content production, code generation, malware troubleshooting, and the refinement of phishing or fraud workflows. Hijacked API keys may also allow actors to consume compute resources at the victim’s expense, bypass usage restrictions tied to their own identities, and access more capable models or enterprise-grade infrastructure. In this sense, stolen AI access is not merely another credential commodity. It can function as an operational force multiplier across multiple stages of the attack lifecycle, making its abuse both expected and potentially more impactful than many traditional forms of account compromise (Figures 7 and 8).

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The impact on organizations can be serious as AI accounts may contain proprietary information such as prompts, uploaded files, source code, legal drafts, customer data, internal summaries, product plans, meeting notes, investigative material, or strategic analysis. If compromised, the exposure extends beyond the credential itself. Enterprise AI accounts and AI-related access tokens should therefore be treated like cloud credentials, developer secrets, email accounts, or administrative SaaS access.

Deepfake services: From impersonation to KYC bypass

Deepfake services have become one of the criminal AI market’s most important adjacent segments, particularly in fraud, synthetic identity creation, onboarding abuse, and KYC bypass. These services are marketed not as experimental technologies, but as practical fraud enablers. Common offerings include face swaps, voice cloning, fake selfie generation, synthetic profiles, document manipulation, virtual camera injection, video-call impersonation, and full onboarding bypass packages (Figure 9). Their significance stems from the fact that many digital platforms continue to rely heavily on remote identity verification and visual trust cues.

The purpose of bypassing KYC controls is to create, validate, or access accounts that should not exist or should not be available to the offender. Once established, such accounts can support money laundering, mule activity, romance scams, investment fraud, payment abuse, sanctions evasion, account resale, and marketplace manipulation. The threat is no longer limited to static fake images. Attackers can combine face swaps, synthetic video, animated media, and virtual camera injection to impersonate real individuals during onboarding or verification.

Deepfake services also strengthen broader fraud operations. Romance scams, fake recruitment schemes, executive impersonation, vendor fraud, and investment scams all become more persuasive when synthetic voice or video is added to the deception chain. These services should therefore be understood as part of the same criminal AI capability stack. LLMs generate scripts, refine pretexts, localize language, and support interaction at scale. Stolen data enhances personalization. Deepfake tools add the visual and audio layer that increases trust and makes deception harder to detect. Together, these capabilities form a more complete deception architecture.

Organizational impact and defensive priorities

For organizations, the impact of AI-enabled cybercrime is both economic and operational. The main concern is not the sudden arrival of fully autonomous AI hacking, but the steady increase in attacker productivity, deception quality, operational flexibility, and post-compromise efficiency.

This last concern is important to note. Once attackers obtain data, AI can help them review it more quickly and more systematically. Models can summarize large document sets, identify sensitive or monetizable material, extract victim-specific details, and support tailored extortion or fraud. This does not require a purpose-built criminal model. It requires access to a capable model, relevant data, and a clear criminal objective.

At the same time, enterprise AI environments are becoming part of the attack surface. AI accounts, API keys, prompts, uploaded files, connectors, retrieval systems, internal knowledge bases, and agentic workflows can all expose sensitive business information if they are compromised, misused, or poorly governed. These assets should therefore be managed with the same seriousness as other critical systems, including clear ownership, least-privilege access, logging, monitoring, retention rules, and periodic access reviews.

Organizations should respond by treating criminal AI as a challenge of trust, identity, workflow security, and data governance, rather than only as a malware issue. High-risk business processes should be reinforced with stronger approval controls, transaction verification, segregation of duties, and out-of-band confirmation, especially for financial transfers, access changes, sensitive data requests, and executive communications.

Phishing and fraud defenses must also adapt. Poor grammar and obvious language errors are no longer reliable indicators of malicious activity. Organizations should assume that many adversaries can now generate polished, localized, and credible communications at scale. Detection should therefore rely more heavily on behavioral indicators, sender validation, process anomalies, identity verification, and transaction integrity than on superficial language cues.

At the same time, organizations should prepare for AI-assisted post-breach exploitation by improving data minimization, segmentation, access controls, monitoring, logging, and incident response planning. They should also monitor the broader underground capability stack, including jailbreak services, stolen AI accounts, and synthetic media tooling, because these increasingly shape attacker tradecraft in practice.

The market will likely see more bundling of text generation, translation, impersonation, data analysis, and synthetic media into a single criminal offering. It will also likely see continued abuse of legitimate AI platforms alongside wrapper-based underground services. The ecosystem will likely remain uneven, opportunistic, and hype-heavy, while becoming strategically important because it makes cybercrime easier to execute, scale, and detectFor organizations, the main risk is not only higher financial loss, but also the growing operational strain created by AI-assisted attacks that are faster, more scalable, and harder to triage.

Enterprise AI accounts, API keys, prompts, uploaded files, connectors, retrieval systems, internal knowledge bases, and agentic workflows should be managed as critical assets, with clear ownership, least-privilege access, logging, monitoring, retention rules, and periodic access reviews. Sensitive data should be exposed to AI systems only when there is a clear business need, especially when AI tools connect to email, cloud storage, code repositories, customer databases, financial systems, or external services. High-risk AI connectors and workflows should be inventoried, risk-ranked, and monitored for abnormal access, bulk data movement, privilege escalation, or unauthorized agent actions.

 As phishing tactics become better, core controls should include MFA, phishing-resistant authentication, conditional access, DLP, EDR/XDR, API security monitoring, secrets scanning, prompt and output filtering, and model-access controls. Incident response plans should also cover stolen AI accounts, exposed prompts, compromised API keys, leaked embeddings, abused connectors, and sensitive data retained in AI workspaces.

The organizations best positioned for the next phase will be those that integrate AI risk into existing security governance rather than treating it as a separate technical issue. As criminal use of AI becomes part of everyday attacker tradecraft, resilience will depend on the ability to verify identity, control access, protect data flows, monitor AI-enabled workflows, and maintain human oversight over high-impact decisions. The future defensive priority is therefore not to predict every AI-enabled attack, but to build security architectures that remain reliable when attackers become faster, more persuasive, and more efficient.

Wade Woolwine is Senior Director, Product Security at Rapid7.

Announcing OpenAI's Trusted Access for Cyber program

CIOs and CISOs are telling us the same thing in different ways: Advances in frontier AI are accelerating the threat environment and putting pressure on security operating models built for a different pace. Vulnerabilities can be discovered faster, exploitation windows are shrinking, and attackers are increasingly using automation to move with greater speed and scale. For defenders, this changes the value equation. The premium is no longer only on detecting threats faster after they emerge, but on moving earlier: Reducing exposure, validating risk, strengthening detection, and remediating at scale before attackers can take advantage.

This is why Rapid7 is excited to be included in OpenAI’s Trusted Access for Cyber program and their announcement today. OpenAI’s approach recognizes that advanced AI can help verified security teams move faster on legitimate defensive work, from triage and detection to validation, patching, malware analysis, and detection engineering. It also recognizes that some specialized cyber workflows require stronger verification, monitoring, and feedback loops.

As Corey Thomas, CEO of Rapid7, shared:

“Security leaders are under pressure from every direction: More vulnerabilities, faster exploitation, and increasing business pressure. Through OpenAI’s Trusted Access for Cyber program, Rapid7 is exploring more ways to accelerate the shift from reactive to preemptive security. To stay ahead of attackers, defenders must proactively reduce exploitability and detect with machine-scale speed and precision. We’re working with OpenAI to equip security teams with advanced capabilities that will meaningfully improve their cyber resilience.”

AI in security: Not just faster discovery

For Rapid7, this moment is about more than faster vulnerability discovery. AI is creating new pressure across the entire security lifecycle, from vulnerability validation, prioritization, disclosure, and remediation to threat and exploitation detection. Security infrastructure built for human-speed discovery now needs to operate in a machine-speed world, with enough context, governance, and accountability to help defenders act with confidence.

Finding risk is only the beginning. Security teams need to understand which vulnerabilities and misconfigurations are truly exploitable, which systems and business services are affected, what compensating controls are in place, how remediation should be prioritized, and where detection coverage is needed. CISOs also need confidence that advanced AI is being applied responsibly, with clear guardrails, measurable outcomes, and accountability.

Our work with OpenAI will help us explore how frontier AI can strengthen three critical areas. First, it can support the identification of vulnerabilities in our own products and code earlier in the development lifecycle. By accelerating secure code review, surfacing risky patterns, supporting root cause analysis, reviewing patches, and giving engineering teams faster feedback, AI can help reduce risk before issues reach production.

Second, it can advance vulnerability research and exploitation analysis. Rapid7 has long-standing expertise in vulnerability intelligence, exploitability research, and offensive security with Rapid7 Labs. Frontier AI can help researchers reason across unfamiliar code, map affected surfaces, build safe reproduction harnesses, validate severity, and turn findings into practical remediation guidance.

Third, it can expand AI-driven red-teaming. As AI becomes more embedded in enterprise systems and security operations, it must also be tested adversarially. We see an opportunity to use AI to strengthen red-team workflows, explore attack paths, validate controls, and help defenders understand where exposure could become real-world risk.

Artificial intelligence in use at Rapid7

We are already seeing this potential inside our own security operations work. In support of our Agentic SOC initiatives, Rapid7 has designed and implemented a system that uses machine learning to surface threat- and risk-relevant events from raw log and telemetry data. By using frontier AI models, including OpenAI’s GPT-5.5, to support initial triage and escalate only relevant events to SOC analysts, we have seen a 25% reduction in time spent chasing false-positive events in the queue.

This is not about replacing human expertise. It is about giving defenders better leverage in a world where attackers, businesses, and technology are all moving faster. The shift from reactive to preemptive security, and from human-scale processes to machine-scale defense, is not a marketing reframe. It is becoming the only viable path for teams that need to anticipate where attackers will move next, prioritize the exposures that actually matter, and respond at the speed of modern attacks.

AI may accelerate discovery, but cyber resilience depends on what happens after discovery. Customers need to unify their data, apply AI with the right context, drive remediation at scale, and translate security activity into measurable outcomes. That is where Rapid7 is focused. Across the Command Platform, Rapid7’s AI capabilities are built to help security teams detect threats and anomalies at scale, reduce noise, optimize SOC workflows, and make faster, more confident decisions.

By unifying Exposure Management and Detection and Response on the Command Platform, and combining AI-driven operations with the depth of expertise we have built over 25 years, Rapid7 is giving customers a more coherent way to reduce risk, disrupt attackers, and build durable cyber resilience. Learn more about Rapid7’s AI capabilities.

Security teams are dealing with a different kind of pressure now. It is not just the volume of alerts or the pace of attacks, but also the gap between what teams can see and what they can act on with confidence.

That gap shows up in different ways. Threats move across identity and cloud in ways that are difficult to track, exposure data exists but often sits disconnected from response, and AI is being introduced into workflows without a clear role in decision-making.

This year’s Rapid7 Global Cybersecurity Summit brings those threads together as part of the same operational solution.

1. You need a clearer view of how attacks actually unfold

A lot of detection strategies still assume attacks follow a clean path. In practice, they do not. They start in one place, move quickly, and often rely on small gaps rather than obvious failures.

Sessions like The Reality of Running a SOC in 2026 break this down in detail, looking at how attacks begin with things like identity misuse or cloud misconfiguration, then evolve as defenders try to keep up. That matters because it changes how detection should be designed. Coverage alone is not enough if teams do not have the context created by strong exposure management to interpret what they are seeing.

That same idea carries into Inside the Modern SOC, where a real investigation is followed from first alert to outcome. It is a useful reminder that detection is only part of the problem.Deciding how to respond, and doing it quickly, is the critical next step.

2. Exposure only matters if it connects to action

Most teams already have some form of exposure management in place. The challenge is making it useful. A long list of vulnerabilities does not help much if it is not tied to how risk actually shows up in the environment.

Sessions like Beyond the Vulnerability List and From Cloud Exposure to Runtime Attack focus on that connection. They look at how exposures turn into active threats, often before any alert is triggered, and how teams can use that information to prioritize earlier.

Here’s the part people miss. Exposure is not just about knowing what is wrong. It is about understanding what matters now, based on how the environment is being used and how attackers are likely to move through it.

3. AI is only useful if it improves decisions

AI is already part of most security conversations, but the reality is nuanced. In some cases it helps reduce noise and speed up investigations. In others, it creates new questions around trust and transparency.

The AI Dilemma: Automating Defense Without Surrendering Judgment tackles this directly. It looks at where AI is helping in real SOC workflows, where it can get in the way, and why explainability matters if teams are going to rely on it. The discussion is grounded in how analysts actually work, not just what the technology promises.

There is also a broader point here. Attackers are using AI as well, which means the balance between speed and accuracy is becoming more important on both sides.

Join the conversation

Across these sessions, the common doesn’t stem from any single technology. It is how teams connect signals, context, and decisions in a way that holds up under pressure, which shows up in how threats are understood, how exposure is prioritized, and how AI is applied. It is also why the summit is structured the way it is, moving from shared context on day one into more focused, role-based sessions on day two.

More sessions and speakers will be added in the coming weeks, but the direction is already clear. Security operations are shifting toward earlier decisions, better prioritization, and fewer assumptions.

If your work touches AI, threat detection, or exposure management, this is where those conversations start to come together.

Join us May 12–13 and see how teams are approaching it in practice.

Register now.

Security teams want more from their data than APIs and one-off reports.

They want to ask better questions, move faster, and bring security context into the workflows they are already building. That’s especially true as more organizations experiment with private AI assistants, internal copilots, and LLM-powered automation. Part of this experimentation is, of course, attempting to lower the pressure on teams that have to figure out how to prioritize the sheer number of actionable vulnerabilities efforts like Project Glasswing are quickly becoming hyper-skilled at spotting.     

That’s why Rapid7 is introducing a free, open-source MCP Server and Agent Skill for Bulk Export. Bulk export is a highly efficient way to access all your Rapid7 data; no more paging APIs, no more verbose output. Bulk Export creates a local offline replica of your data the LLM can efficiently and quickly interrogate, reducing token cost and time to answer questions.

This new MCP and Agent Skill gives customers a standardized way to connect Rapid7 vulnerability and exposure data to AI assistants and custom AI workflows. Built as an open-source bridge, it helps customers bring their Rapid7 data into the tools and experiences that work best for their teams.

Why this matters now

Security teams are no longer just buying tools. They’re connecting systems, shaping workflows, and testing how AI can help analysts, IT teams, and leaders get to answers faster. For many teams, the path from raw security data to usable AI context is still manual. It often means exporting data, building wrappers, shaping queries, and managing custom integrations.

Rather than leave every team to solve that challenge from scratch, we wanted to provide a stronger foundation that is flexible, practical, and easy to extend over time. With projects like Metasploit and Velociraptor, Rapid7 is committed to Open Source, and by sharing with the broader community we hope to accelerate velocity and ensure we’re able to incorporate more use cases and fixes. These processes also give customers full visibility of the code running and tools used, ensuring data privacy and allowing the user to do with their data what they please.  

What MCP does

Model Context Protocol, or MCP, is an emerging standard for helping AI systems interact with external data and tools in a structured way.

In practical terms, it gives AI assistants a cleaner way to ask questions, retrieve data, and work with systems beyond the model itself. For customers, that means less custom glue code and a more consistent way to use security telemetry in AI-driven workflows.

That matters because many security reporting and analysis workflows still assume a high technical bar. Answering a simple question can require custom queries, SQL knowledge, or dashboard work. But the people who need those answers aren’t always security specialists. They may be IT partners, compliance stakeholders, or executives who want clarity but might not need to understand the underlying query logic.

The MCP server helps lower that barrier: Instead of starting with raw exports and working backward, teams can start with the question they need answered.

The bigger picture: MCP and CTEM

This approach also aligns with the broader shift toward continuous threat exposure management, or CTEM. 

CTEM is about helping teams move beyond point-in-time findings toward a more continuous, contextual understanding of risk. That requires security data that can be accessed, connected, and used across the workflows teams rely on. 

Bulk Export helps make that possible by giving customers more flexibility in how they use Rapid7 data. The open-source MCP server makes it easier to bring that data into AI-assisted and custom workflows.

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That can support more continuous exposure management workflows by making it easier for teams to triage vulnerability and exposure data. For example, an analyst facing a large queue of new vulnerabilities could use LLM assistance to quickly narrow in on the findings most likely to need attention first. Instead of manually working through exports and queries, they could ask natural-language questions to surface the exposures tied to critical assets, unresolved remediation work, or other signals available in the data.

From data portability to AI-ready interoperability

Bulk Export was already an important step toward giving customers more control over their data. It made it easier to extract and use security telemetry in external tools and analytics environments.

The open-source MCP server builds on that foundation: Instead of using exported data only for dashboards or custom reporting, customers can now use that same data in AI-native experiences. That includes internal assistants, private copilots, workflow automation, and natural-language exploration of vulnerability and exposure data. This makes existing security data easier to use in the environments customers are already investing in.

How it works

At a high level, the architecture is straightforward. Using the Agent Skill, your LLM runs the MCP server locally and automatically prepares the environment by performing the bulk export and loading the data into a local file store. The Agent Skill provides the schemas and knowledge, with the MCP providing the tools to access this data. The LLM then will answer any question by querying, summarizing, and synthesising data locally – an extremely fast and simple process that's for the LLM. 

Depending on the data a customer exports, answers can include vulnerability records, asset data, remediated vulnerabilities, and policy-related results.

The point here isn't just that a model can access the data, it’s that an open-source layer helps customers inspect, adapt, and extend over time, empowering teams to control how that connection works in their own environment. 

What customers can do with it

This opens the door to practical use cases, including:

• Using LLM assistance to triage vulnerability data faster 

• Asking natural-language questions to spot exposure and remediation trends

• Investigating which assets are tied to the most urgent vulnerabilities

• Understanding what changed over time without manual analysis

• Exploring policy failures without building manual queries

• Feeding Rapid7 telemetry into private AI assistants and internal workflows

• Making reporting more accessible for non-technical stakeholders

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For teams already trying to operationalize AI, this creates a lower-friction path. Instead of building every integration from the ground up, they can start with a reusable bridge and focus on the workflows they want to enable.

A better path from data to action

Security data only creates value when teams can use it. For many organizations, turning raw telemetry into timely answers is still harder than it should be. Analysts need speed. Leaders need clarity. Builders need flexibility. And more customers want security data that works inside the tools and workflows they already rely on.

The open-source MCP server for Bulk Export is designed to help make that possible.

Bulk Export helps customers take control of their data. This is the next step: helping them put that data to work in AI-ready security workflows.

Ready to explore it for yourself? Visit the Rapid7 Bulk Export MCP Server project on GitHub to learn more and get started.

Anthropic’s Project Glasswing has sparked plenty of discussion about what AI might soon do for vulnerability discovery, but the more useful question for most security teams is how to prepare for, and more importantly seize the opportunity of, what comes next.

 As we wrote in our earlier blog, What Project Glasswing Means for Security Leaders, AI is becoming more capable of finding software flaws. The pressure that follows lands on the teams responsible for deciding what matters, validating risk, assigning ownership, and getting remediation moving across environments that were already hard to manage. We believe that the organizations that will benefit most from the next wave of AI will be the ones that understand their environment well enough to use these emerging AI models with intent, rather than layering them onto immature processes and hoping that speed alone will solve the backlog.

What this moment means for security teams

The number of publicly tracked software vulnerabilities has broken records almost every year over the last decade, while supply chain risk has continued to rise. Most teams were already feeling the strain of more findings than they could process cleanly. The Common Vulnerabilities and Exposures (CVE) program, the standard system for identifying and tracking known vulnerabilities, recorded 48,185 disclosures in 2025, a 20% increase over 2024, with roughly 40% of those disclosed vulnerabilities rated high or critical. 

The pace in 2026 was already working out to hundreds of new CVEs per day when those figures were cited. That tells you something important about the current environment: the challenge has not necessarily been  a lack of findings, but instead converting a growing stream of findings into measurable risk reduction.

The reality is that very few organizations are going to hand a model free rein over their most sensitive environments the minute those capabilities become more widely available. Trust will be built in stages: early adoption is much more likely to focus on backlog reduction, triage support, patch testing, and repetitive lower-tier remediation work that consumes time without carrying the same level of operational risk as the most critical systems in the business. That is a more realistic starting point, and it leads to a more useful question. Before teams apply AI more broadly, they need to understand their environment well enough to use it intentionally.

Establish the foundation before layering in AI

The promise from Project Glasswing and almost every other AI-powered security initiative is quite similar: leverage AI to identify patterns, summarize risk, suggest fixes, and speed up repetitive work. Regardless of technology, success  still depends on how well an organization understands its environment, the context around each finding, and the process used to act on it. 

A model can generate more output than a team ever could on its own, but that output becomes noise if the organization cannot answer basic questions about scope, ownership, criticality, and exposure. Teams need a clear, continuously updated picture of the environment before they can decide where AI should be applied, what should remain human-led, and which parts of the backlog are safe to push through more automated workflows.

The AI landscape is already shifting fast, and it will keep shifting, which is why this moment should prompt a more preemptive and resilient strategy rather than another round of tooling hype. Chasing each new capability as it arrives will inevitably force teams to keep reorganizing around the latest announcement. A stronger path is to get the foundation right first - understand the environment, the attack paths, and the assets that matter most; but most importantly, establishing the process and the people behind making these decisions. Then use AI where it meaningfully improves speed, consistency, and focus.

Why Attack Surface Management should be part of that foundation

A strong foundation starts with visibility. Security teams need a live picture of what exists in the environment, what is exposed, how assets connect to one another, and which systems carry the greatest business impact if something goes wrong. That is where Attack Surface Management becomes central. Rapid7’s approach through Surface Command is built around a continuous view of the attack surface across the digital estate, which helps teams understand where exposures sit and how they relate to internet-facing, business-critical, or otherwise high-impact systems.

That matters for AI adoption just as much as it matters for day-to-day security operations. Teams cannot apply AI strategically if they are guessing about which parts of the environment are lower priority, which assets belong to which owners, or where a newly disclosed flaw could create real business risk. A better view of the attack surface gives organizations the context they need to segment the problem properly. That makes it far easier to start with the right use cases, whether that is backlog reduction in lower-impact systems, targeted prioritization of exposed assets, or faster triage where the risk picture is already well understood.

Ownership is part of that foundation too. Remediation slows down when no one can quickly identify who owns the affected application, environment, or workflow. Security teams already lose time there today, and AI will only make that bottleneck more visible if it starts surfacing issues faster than organizations can assign them. Attack Surface Management helps turn that ambiguity into something more actionable by tying exposure to environment context and likely ownership.

How Vulnerability and Exposure Management turns visibility into action

Once the environment is understood, teams still need a way to move from findings to outcomes. That is where Vulnerability and Exposure Management becomes the operating layer that keeps the work grounded.

The biggest value here is not simply collecting more vulnerability data. It is targeted prioritization and validation. When a disclosure lands, teams need to know whether the issue affects an exposed asset, whether there is evidence of exploitation or attacker interest, whether the impacted system is business-critical, and whether existing controls already reduce some of the risk. That is the kind of context that helps organizations decide what deserves immediate attention and what can be handled through a normal remediation cycle.

This is where artificial intelligence can help move remediation forward faster. Instead of asking teams to manually connect exploit signals, asset criticality, and vulnerability intelligence on their own, AI can distill that context directly in the remediation workflow. That makes it easier to understand why an issue matters, what the likely impact is, and what to do next, which shortens the gap between discovery and a confident decision on how to respond.

We expect most organizations to use AI to assist with, or in some cases take over, lower-tier triage, backlog cleanup, summary generation, and patch support in areas where the workflow is already established and the blast radius is more manageable. Human experts still stay closest to the most critical business logic, the most sensitive environments, and the most complex remediation paths. That is a practical adoption model, and it only works when the organization already has enough structure in place to know where those boundaries are.

Curated vulnerability intelligence changes the quality of decisions

That kind of deliberate adoption only works when teams can make better decisions, faster. Security teams need more than severity scores and a long list of CVEs. They need enough context to understand what matters, what can wait, and where action will reduce real risk fastest. As Rapid7 outlined in The Power of Curated Vulnerability Intelligence, the goal is to identify the vulnerabilities that actually matter and give teams enough context to act with confidence.

That intelligence provides a form of validation that most teams need badly as disclosure volume rises. It helps answer whether a finding is tied to active attacker interest, whether proof-of-concept activity is public, whether the asset is exposed, and whether delaying a patch creates unacceptable risk. It also supports the decisions that happen in the gap between discovery and full remediation. When a patch is delayed because of change controls, testing constraints, or lack of a vendor fix, teams still need to reduce exposure. Curated intelligence helps them decide whether to use segmentation, access restrictions, configuration changes, added monitoring, or virtual patching while the longer-term fix is being worked through.

That is one of the clearest ways Rapid7 helps customers move from data to outcomes. Intelligence is fused into the workflow so teams can prioritize with more precision and validate their actions against real threat context, not just generalized scores.

How runtime and remediation fit into the broader AI story

There is another part of this story that matters as organizations think more seriously about AI-driven security operations. As AI shapes the way teams handle exposures earlier in the lifecycle, context of application at runtime matters more too.

To make that foundation complete, organizations need to look beyond static posture and bring runtime validation into the picture. When teams can identify which vulnerabilities and misconfigurations are actively exploitable in production, and map sensitive data and identity access to real-world attack paths, they get a much clearer view of actual risk. Security teams need to understand what is vulnerable, how systems behave when live, and where unusual activity may suggest a problem is moving toward exploitation. With that runtime context in place, teams can spend less time chasing theoretical vulnerabilities and more time focusing on the exposures that are actively creating risk in live environments. 

That connection between exposure, intelligence, remediation, and runtime behavior is where AI starts to become genuinely useful rather than simply impressive. It supports a more intentional model of security decision-making, one that narrows the gap between what is found, what matters, and what happens next.

What security leaders should do now

This is a good time for security leaders to step back and ask a more disciplined set of questions.

• Do we understand our environment well enough to direct AI toward the right problems? 

• Can we clearly separate higher-risk, higher-impact assets from the parts of the backlog that are mostly operational drag? 

• Is threat intelligence embedded in how we interpret findings, or are we still depending too heavily on raw severity? 

• Can we identify ownership fast enough for AI-assisted triage to result in meaningful action? 

• Are compensating controls part of the plan when remediation cannot happen immediately?

Those questions shape the quality of everything that follows.

Glasswing creates a real opportunity for security teams that are ready to use AI with more intention. AI can move work forward faster, reduce manual drag, and absorb classes of issues that currently consume time without improving outcomes. The teams that benefit most will not be the ones that rush to apply new models everywhere. They will be the ones that understand their environment, have a clear view of their attack surface, have mature enough workflows to apply AI where it makes sense, and can measure whether the actions taken actually reduced exposure.

Rapid7’s approach to building resilience is grounded in those same needs. Attack Surface Management provides the environmental foundation, Vulnerability Management drives prioritization and action, curated vulnerability intelligence strengthens validation and decision-making, AI-generated remediation insights compress the time from discovery to the next step, and runtime security adds context where live behavior matters. Together, those pieces help customers build a security program that is ready for AI rather than constantly reacting to it.

Security teams are flooded with logs, yet every alert demands fast, accurate context. In Verizon’s 2025 Data Breach Investigations Report [1], they analyzed 22,052 security incidents, of which 12,195 (55%) were confirmed breaches, underscoring how much activity teams must sift through to find what matters.

In practice, that means dozens of investigations per shift, each requiring fast judgment with incomplete context. A 2024 SANS survey shows that SOC teams report alert volume, limited context, and lack of automation continue to slow investigation and response [2].

Speed suffers. So does consistency.

Turn raw logs into a clear narrative

AI-Powered Log Summary in Rapid7 Incident Command transforms raw log data into a clear, concise narrative directly within the investigation workflow. Analysts see what happened, why it matters, and what to do next in seconds, not minutes.

Instead of decoding logs line by line, analysts get:

• Instant identification of who initiated the activity.

• Fast understanding of exactly which actions occurred.

• Clarity into when and where events unfolded.

• Connectivity into why that behavior matters.

Analysts stay grounded in the original data, but they no longer have to fight through it to find answers. The summary provides immediate orientation and focus, keeping their focus on what to do next.

Built for real SOC workflows

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AI-Powered Log Summary is embedded directly into the log search workflow. No pivoting, and no context switching. With a single action, analysts generate a contextual summary tailored to their results in seconds. That means faster investigations without breaking flow.

Summaries can be shared with teammates or leadership to communicate findings quickly, without rewriting technical details into plain language. Everyone stays aligned on what happened and what comes next.

AI integration in action

Rapid7 leverages the best available technology to protect our customers' attack surfaces. Our mission drives us to keep abreast of the latest AI advancements to deliver optimal value to customers while effectively managing the inherent risks of the technology. Integrating AI into our core processes enhances our operational security and underscores our commitment to ethical innovation. 

At Rapid7, we are dedicated to leading responsibly in the AI space, ensuring that our technological advancements positively contribute to our customers, company, and society. Read more about how our TRiSM (Trust, Risk, and Security Management) is a foundational strategy that guides us in navigating the intricate landscape of AI with confidence and security.

Less noise, more impact

By reducing time spent parsing logs, teams can focus on what matters: containment, remediation, and proactive threat hunting.

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This brings analysts:

• Faster triage and investigations.

• More consistent analysis across shifts.

• Lower cognitive load during high-volume periods.

• Clear communication to stakeholders.

Rapid7 is at the vanguard of integrating AI into its products to accelerate outcomes for our customers, with a particular focus on amplifying analyst impact and bringing speed and clarity to SOC operations throughout the threat detection and response lifecycle. 

That is how modern SOC teams move faster. Visit the Incident Command page for more information.

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[1] Verizon 2025 DBIR

[2] SANS 2024 SOC Survey

Anthropic’s Project Glasswing matters because it offers an early look at how quickly software flaws may soon be found, validated, and potentially turned into viable attack paths, even if that capability is currently limited to a closed partner program. Anthropic says its restricted Claude Mythos Preview model has already identified thousands of high-severity vulnerabilities, including flaws in major operating systems and browsers, and in some cases developed related exploits autonomously.

Some early coverage has emphasized the risks and need for restraint in deploying capabilities like this, and for most organizations, it won’t immediately change day-to-day security operations. What it does offer is a signal of where the industry may be heading: a future where discovery moves faster, and where the pressure shifts to everything that follows, including prioritization, remediation, validation, and response. Glasswing feels less like the storm itself and more like the first sign that the radar is getting better faster than the emergency plan. How well can we handle what comes next?

What is Project Glasswing?

Project Glasswing is Anthropic’s new defensive security initiative built around Claude Mythos Preview, a model the company is not releasing publicly because of its cyber capabilities. Anthropic says the preview is being provided to a limited set of organizations, including AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks, with access also extended to more than 40 additional organizations. Anthropic has also committed up to $100 million in usage credits and additional support for open-source security work. 

That makes this more than another AI feature release. Anthropic is effectively signaling two things at once. First, there is a meaningful backlog of serious, undisclosed vulnerabilities still out there. Second, capabilities like this are sensitive enough that broad public release would be irresponsible right now. For security leaders, the message is not that AI replaces human researchers. It is that AI is becoming materially more useful in vulnerability research, and defenders should be thinking now about how they will handle what comes next.

Why this matters to vulnerability management

It would be easy to read this as a story about faster vulnerability discovery alone. That misses the more important point. If Anthropic’s claims are directionally right, the immediate pressure does not land on discovery alone. It lands on everything downstream of discovery: asset context, exploitability analysis, ownership, compensating controls, patching, exception handling, validation, and detection coverage. In other words, the harder part of security becomes more obvious.

That matters because most enterprise programs do not struggle to generate findings. They struggle to decide which findings matter first, who should act, what can wait, and whether remediation actually reduced exposure. If AI pushes vulnerability discovery into a new gear, weak operating models will feel that pressure first. Backlogs get bigger. Teams drown in queues. Fix rates do not keep pace. Risk stays put. That is not a model problem. It is an execution problem. 

This is why security leaders should be careful with the framing. The headline is not “AI found bugs, therefore security improves.” The headline is that the bottleneck may be moving downstream even faster than expected. That raises the value of programs that connect exposure management, remediation, and runtime defense instead of treating them as separate activities. 

What Anthropic’s examples really tell us

Some of the reported examples are striking. Anthropic and media reports say Mythos Preview found a 27-year-old OpenBSD vulnerability, a 16-year-old FFmpeg flaw that reportedly evaded millions of automated test executions, and multiple Linux kernel vulnerabilities that could be chained together. Anthropic has also said the model reproduced vulnerabilities and built proof-of-concept exploits at a high success rate in testing. Even if individual examples get debated over time, the pattern is the important part. The model appears to compress several human steps into one workflow, from discovery to validation to exploit construction. 

Security has seen faster discovery before. Fuzzing changed the game. Better automation changed the game. Large-scale bug bounty operations changed the game. What is different here is the combination of reasoning, coding, persistence, and iteration inside a single model loop. If that loop becomes reliable, then defender workflows built for human-speed intake and triage will come under more strain. That does not make coordinated disclosure obsolete. It makes today’s processes look slow.

What CISOs should ask right now

CISOs do not need to decide this week whether Anthropic’s model changes the entire market. They do need to ask a more practical question: if my environment starts surfacing materially more vulnerabilities tomorrow, what happens next?

For many organizations, that answer is uncomfortable. Findings land in multiple tools. Asset inventory is incomplete. Internet exposure is only partly understood. Ownership is fragmented. Patch cycles are slow. Exceptions pile up. Security teams cannot easily prove that a fix changed reachable risk in the real environment.

That is where this news becomes relevant. AI-driven discovery does not reduce the need for an exposure-led security model. It increases it. The organizations that benefit most will not be the ones with the biggest pile of findings. They will be the ones that can connect those findings to business-critical assets, internet exposure, identity paths, existing detections, remediation workflows, and validation. 

A good board-level translation is that faster discovery only has value if the organization can prioritize effectively, remediate quickly, and prove that the fix reduced real exposure. Otherwise, the result is more volume and more noise.

What engineers should take away from Project Glasswing

For engineers, this announcement is less a reason to either celebrate or dismiss the technology than it is a sign that defensive research workflows may change quickly if capabilities like this spread more broadly. Today, Glasswing is still limited to a small group of trusted partners, so this is not yet a shift most engineering teams will feel directly in their daily work. What it does offer is an early look at where software security may be heading.

AI-assisted discovery is likely to become more common across secure development, code review, infrastructure testing, and open-source maintenance. That creates real opportunities. Models can help explore deep code paths faster, challenge assumptions earlier, improve reproduction, and generate more detailed reports than many conventional workflows produce today.

The harder question is what comes next. If AI can generate more findings and more exploit hypotheses, engineering teams will need stronger intake, validation, and prioritization discipline, not less. Triage quality, deduplication, severity context, reproducibility, and ownership all become more important as discovery speeds up. Many maintainers and internal product security teams already struggle with volume, and machine-generated reporting could make that problem worse if workflows do not mature alongside the tooling.

At the same time, that is only one side of the equation. If models can help find bugs faster, they may also help defenders confirm impact, suggest code changes, support patch development, and reduce some of the manual effort that slows remediation today. In the longer run, the same AI shift that increases pressure on defenders may also help them absorb some of that pressure. The real issue is not whether AI adds more findings. It is whether teams can use it to shorten the full path from discovery to decision to verified fix.

The best engineering response, then, is not to argue about whether these models are impressive. It is to improve the operating path around them. Can the team confirm impact quickly, tie a flaw to reachable attack surface, deploy a patch or control change, and verify that exposure is actually reduced in production? If that chain does not improve, faster discovery alone will not deliver much value.

What this means for the next phase of security

Anthropic’s decision to restrict access is understandable, but it also underscores a harder truth - capabilities like this rarely stay contained for long. Whether through competitors, open customization, or less restrained releases, the broader industry should assume similar models will become more widely available in the near term. For most organizations, this is not a market-wide operational shift today. It is a warning of what may be closer than it appears.

That signal arrives at a time when many security operations teams are already under strain. Most can investigate only a fraction of the alerts and exposures their environments generate, which keeps them in reactive mode, manually triaging high-priority signals across fragmented telemetry while scale and consistency remain difficult to achieve. Many promises of AI super-productivity have not yet translated into day-to-day operational relief. That is part of what makes Glasswing worth paying attention to. It points to a future where discovery may improve faster than most response models do.

It also points to an opportunity. If AI can compress parts of vulnerability research, the same broader class of capabilities may eventually help defenders improve prioritization, investigation, remediation, and validation as well. That is where the next phase of security is likely to be decided. Not in whether organizations can generate more findings, but in whether they can use AI to make response workflows faster, more consistent, and more precise.

From our perspective, that raises the operational bar for defenders. If discovery gets faster, organizations will need to shorten time to detect, accelerate time to patch, and manage vulnerability backlogs with far more urgency than they do today. That starts with a threat-led view of the environment. Teams need to understand which weaknesses are most exposed, most exploitable, and most likely to matter in real attack paths so they can prioritize action based on actual risk, not just queue depth.

That is the practical lesson from Glasswing. It feels less like the storm itself and more like the first sign that the radar is getting better faster than the emergency plan. For most organizations, the announcement does not change the queue tomorrow morning. What it does change is the urgency of preparing for a future in which discovery, triage, and response may all begin moving at a very different pace.

In the latest episode of Rapid7’s Experts on Experts, I’m joined by Rapid7 CEO Corey Thomas for a candid conversation about where AI is genuinely changing security operations, and where the hype still outruns reality. The short version is that AI is already improving productivity in software development, but the bigger shift for security leaders is what it can do with telemetry at scale. As Corey puts it, no team of humans can process all security telemetry, all the time, across an entire environment. That gap is where AI can help, but only if the inputs are right.

We also dig into what this means for Managed Detection and Response (MDR), and why the market is moving from “watch a subset of signals” toward monitoring the full environment, 24 x 7. The catch is that raw volume is not the goal. The goal is a comprehensive data set that enables decision making under pressure, with enough context to act early.

AI is only as good as the context behind it

One theme that kept coming up in our conversation is trust. Corey explains why earlier automation and SOAR efforts struggled. They followed strict rules, but security rarely behaves in strict patterns. When something looked similar but required a different response, teams hesitated to rely on automation. The dynamic rule making that newer AI models provide can help, but only if fueled with the right context.

Corey breaks “context” into practical components: understanding what technologies are deployed, how they are configured, what controls exist, what vulnerabilities are present, and what activity is actually happening across those systems. Without that full picture, teams spend time chasing the wrong risks. He compares it to buying earthquake insurance without knowing where you live. If you are in California, it might make sense. If you are in Florida, hurricane coverage is the real concern. Context tells you which risk actually matters.

Preemptive MDR is the shift CISOs should plan for now

Where the conversation gets especially relevant for 2026 is the move from reactive to preemptive security. To frame the change in plain terms: reactive posture waits for alerts, while leaders want partners who anticipate and identify risks earlier.

Corey describes preemptive MDR as an attack surface discipline. It starts with understanding the full attack surface, spotting where attacks are likely to occur, and identifying the most attractive exposures in the environment. The operational step is what matters: identifying those exposures quickly, prioritizing realistically, and having preset remediation and response plans ready before the moment hits. Corey is direct about constraints, too. No organization can remediate everything all the time, but better planning and efficiency are still possible, and business expectations of security leaders are rising. He also notes that government and regulators are pushing in the same direction, and that Gartner and other analysts are reinforcing the shift toward anticipation rather than after the fact response.

Cloud scale forces MDR to evolve, especially around identity

We also spent time on the cloud, because it continues to reshape how security programs operate.  Most organizations are building more, faster, across more cloud technologies and identities, and AI only accelerates that pace. Corey’s view is that MDR has to mirror that technology reality. At a baseline, teams need to monitor what their cloud providers already offer. He calls out identity as the harder requirement: understanding identity traffic across the environment, separating legitimate from malicious behavior, and tracking roles and responsibilities so investigations do not happen in a vacuum. If an MDR program is not looking across the cloud landscape, it cannot confidently say it is monitoring the right things, especially in the areas where new bugs and misconfigurations show up first.

Transparency becomes a differentiator when AI enters the loop

As AI becomes more present in triage and investigation, Corey argues that transparency will matter even more. He shares that Rapid7 built MDR with the assumption that customers should be able to log in at any time and audit what is happening in their environment. That level of visibility can be uncomfortable, but it becomes more important as AI plays a larger role in how decisions are made. The presence of AI in MDR programs does not reduce the need for trust, but increases it. And that trust is built through transparency and auditability, not assumption.

That also means being able to show where AI is actually making a difference. It is not enough to say it is working. Teams need to see the impact in real terms.

Corey contrasts that with what he sees as the market default: black box approaches that ask customers to trust the output until something goes wrong. His prediction is blunt and practical. As buyers mature, RFPs will demand the ability to inspect how alerts are processed and how investigations are run, because that is what trust looks like at scale.

Watch the full episode below to hear Corey’s take on what is changing, what is still missing, and why the strongest MDR programs in 2026 will be the ones that plan for preemptive action, not just faster reaction.

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When Anthropic announced Claude Code Security, the market reacted immediately. Several cybersecurity stocks saw sharp drops as speculation spread that AI-powered code security tools could displace traditional security platforms.

The narrative moved quickly: AI is replacing AppSec. AI is automating vulnerability detection. AI will make legacy security tooling redundant. The reality is more nuanced. Claude Code Security is a legitimate signal that AI is reshaping parts of the security landscape. The question is what parts, and what it means for the rest of the stack.

Claude Code Security represents an evolution in AI-assisted static code analysis. It scans codebases, reasons about context, identifies potential vulnerabilities, and proposes fixes for human review. That capability is meaningful, and it reflects the growing role of AI in accelerating software development and improving developer productivity.

What is Claude Code Security 

At its core, Claude Code Security enhances static analysis with contextual reasoning. It analyzes source code and attempts to identify vulnerabilities that traditional pattern-matching scanners may miss. It applies verification layers to reduce false positives and presents findings for human validation.

For development teams, this has clear value. It can improve code hygiene earlier in the lifecycle and reduce noise compared to traditional SAST tools. For enterprises, however, secure code is only one layer of risk.

Modern breaches do not rely solely on poorly written functions. They exploit misconfigurations, excessive permissions, identity gaps, runtime behavior, exposed services, and weak operational processes. These risks exist outside the code repository. AI-assisted static analysis improves one part of the equation. It does not replace the broader security stack.

Why the market reaction tells an incomplete story 

The drop in cybersecurity stock prices reflects an assumption that better code scanning equals reduced need for detection, response, and exposure management platforms. That assumption overlooks how enterprise security actually functions.

The market’s reaction also reflects a broader belief that this is the beginning of widespread AI-driven displacement in security. That view is partially correct. AI is particularly strong in domains where patterns are well understood and repeatable. Application security vulnerabilities often fall into known classes with predictable root causes. In those bounded problem spaces, displacement is real. But it does not extend uniformly across the full security stack.

Code security addresses vulnerabilities before deployment. Enterprise security addresses behavior after deployment. Detection and response platforms monitor identity misuse, lateral movement, cloud misconfiguration, and attacker tradecraft in live environments. Managed detection and response services provide human expertise to investigate and contain incidents when automated controls are bypassed.

Recent reporting of AI-orchestrated intrusion activity reinforces this distinction, showing how attackers can leverage an AI assistant to automate reconnaissance and exploitation steps at speed.. The issue is not the existence of AI, but the absence of layered controls capable of detecting how that AI is being used. Secure code in isolation does not prevent operational abuse. Runtime visibility and response capability remain essential.

These domains overlap, but they’re not interchangeable. Finding an injection flaw in a code repository doesn’t remove the need to monitor for credential abuse, persistence, or post-compromise behavior in production. From my perspective, there’s a meaningful difference between AI that helps developers write safer code and AI that protects a live environment. One works on source code in a repository before deployment. The other has to handle the real world: identity, behavior, lateral movement, and attacker intent across cross-vendor infrastructure that changes in real time. Both matter, but when we conflate them, we create either a false sense of security or a false sense of disruption.

Where AI does belong in security

AI models and agentic systems should serve as purpose-built engines that improve outcomes in real time. AI innovation is a continuous process, not a one-time product release. Rapid7 has invested heavily in AI-driven workflows across our platform and MDR services. Used correctly, AI accelerates triage, enriches alerts, prioritizes risk, and reduces time to action.

In managed detection and response (MDR) environments, for example, AI-driven workflows can help scale investigations and surface high-confidence insights faster, while keeping analysts firmly in control. 

The key distinction is this: AI should amplify human expertise and operational processes, not replace them. That is the philosophy behind how many enterprise platforms are approaching AI today, including the integration of AI features into broader security workflows rather than positioning them as standalone replacements.

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AI can help developers write safer code and identify risky patterns earlier in the lifecycle. It becomes most valuable when its findings are integrated into a broader security context. Enterprise security requires runtime visibility, identity governance, segmentation, and continuous validation across production environments.

AI-driven code and vulnerability tools should be treated as another high-value source of security telemetry and remediation insight. Just as security operations teams ingest third-party alerts into detection workflows or correlate exposure data from cloud and application security tools into a unified risk view, newer capabilities like Claude Code can contribute meaningful signals. The responsibility of security leadership is to ensure those signals are contextualized within a holistic view of risk across the organization.

Secure development matters. So does understanding how code, infrastructure, identity, and runtime behavior interact. The strongest programs will integrate AI-assisted insights into that wider risk model rather than evaluating them in isolation.

In my work, we’re building AI that sits inside the operational loop of a diverse landscape: triaging alerts, enriching investigations, and helping analysts move faster on what matters. That is very different from scanning code before it ships. The real opportunity is not AI replacing security platforms, but AI making the humans running those platforms dramatically more effective. The companies that get this right will not try to automate away human judgment; they will find ways to scale it. 

How security leaders should think about it

For CISOs and senior security leaders, the takeaway should be measured and strategic.

First, recognize the value. AI-assisted code security tools will likely become standard in modern development environments. They can improve quality and reduce certain categories of vulnerability earlier in the lifecycle.

Second, avoid over-indexing on them as a replacement for enterprise controls. Breaches rarely occur because static analysis was unavailable. They occur because exposures persist across identity, infrastructure, and operational layers.

Third, focus on integration. Ask how AI code analysis feeds into broader exposure management. How findings are prioritized. How runtime controls validate that remediations are effective. How detection engineering adapts to new development patterns introduced by AI-generated code.

The path forward to resilience 

Security leaders do not need another debate about whether AI changes security. It does. The question is how to incorporate it without distorting risk priorities.

AI-assisted code analysis should be adopted where it delivers clear value: earlier defect detection, faster remediation cycles, and stronger developer feedback loops. That improves engineering outcomes. It does not, on its own, materially reduce enterprise breach risk.

Enterprise risk concentrates elsewhere. It is found in the systemic exposures that emerge in live environments. That includes complex identity estates, misaligned permissions, overexposed services, and the gaps that exist between deployment and continuous monitoring. These are not source code issues; they are operational realities.

As AI accelerates software delivery, it also increases environmental volatility. More code ships. More infrastructure spins up and down. More integrations connect systems that were never designed to operate together. Risk does not disappear. It shifts and compounds.

The priority for CISOs is alignment. Align AI-assisted development controls with exposure management. Align exposure insights with runtime detection. Align detection with disciplined response. That integration determines whether AI becomes a force multiplier or a source of blind spots.

Organizations that treat AI as an enhancement to an already coherent operating model will extract measurable value. Those that treat it as a substitute for layered controls will not.

Security remains an end-to-end discipline. Code is one layer, but resilience is the objective.

Author note: Laura Ellis, VP, Data & AI at Rapid7, has written about how agentic AI workflows can help MDR teams improve speed and operational consistency while keeping humans firmly in control. Read more about that approach here.