The decision follows BOD 26-04, which directs federal organizations to prioritize vulnerabilities based on real-world risk.
The post CISA Retires Weekly Vulnerability Bulletin in Risk-Based Pivot appeared first on SecurityWeek.
The company plans to expand into continuous cybersecurity, offering security testing across applications and infrastructure.
The post Comp AI Raises $34 Million for AI-Native Compliance and Security appeared first on SecurityWeek.
Point-in-time audits and sampled assessments offer only snapshots; continuous control monitoring provides evidence that security controls are working today.
The post “We Think the Security Control Is Working” Is No Longer Good Enough appeared first on SecurityWeek.
The company will increase its US market presence and will expand its engineering and go-to-market teams.
The post HelmGuard Raises $7.3 Million for Agentic GRC and Security appeared first on SecurityWeek.
I keep hearing the same frustration when I talk with security leaders. The real problem sitting on their desk isn’t finding vulnerabilities. It’s deciding which ones actually matter.
The industry has spent billions on better visibility. We’ve convinced ourselves that if we could just discover more vulnerabilities, collect more data, and ingest more threat intelligence, we’d become more secure. But look around. Organizations still aren’t more secure. They’re just overwhelmed.
Then AI arrived and changed the game entirely. In a matter of months, vulnerability discovery accelerated dramatically. AI systems review code faster than human researchers ever could. They identify weaknesses at unprecedented scale. They scan continuously without the limitations of time, staffing, or attention. The headlines have been everywhere. Government leaders are reevaluating AI laws. CEOs are tossing and turning at night.
But we are focusing on the wrong issue. None of this is actually solving what matters most.
For years, security teams have been drowning in findings. Every new threat feed promised greater visibility. What arrived instead was noise—more data, more alerts, more dashboards, more vulnerabilities. Rarely clarity. Now AI is pouring gasoline on that fire.
The conversations around AI in cybersecurity often get stuck in the wrong place. People debate whether it will help defenders move faster or enable attackers more easily. Both matter, but they are not the core issue.
The real consequence of AI is that it’s exposing something organizations have avoided facing. A vulnerability is not risk, it’s just a clue. Risk emerges when information connects to context: how critical the affected asset is, what controls surround it, how likely exploitation is, the business processes it supports, and what happens operationally if it fails.
Without that context, prioritization becomes impossible. Resources get spent on low-risk issues while mission-critical, vulnerabilities sit unfixed.
Now AI is now making the data volume problem almost impossible to comprehend. An enterprise working with hundreds of software vendors, cloud providers, contractors, and technology partners must investigate every relationship. It’s like a cybersecurity nesting doll where AI continuously identifies vulnerabilities across that entire ecosystem, every minute of every day.
The real challenge today isn’t discovering weaknesses. It’s determining which of tens of thousands of newly discovered weaknesses could actually disrupt operations, impact customers, halt revenue, or create regulatory exposure. Most organizations can’t answer that question quickly.
Some still rank risk using severity scores built for technical teams rather than business leaders. Others rely on manual triage that was already struggling before AI. Many still measure security maturity by how many findings they identify rather than the speed and accuracy of their decisions.
These approaches don’t work anymore. They probably didn’t work yesterday either.
What’s uncomfortable to acknowledge is that AI isn’t creating a cybersecurity crisis. It is revealing one that’s existed for years. The organizations that succeed in this an AI world will transform discovery into judgment faster than their competitors. When AI can find nearly every weakness, security belongs to those who know what to act on. It belongs to those who can connect data to business reality.
That’s the real edge. That’s what separates secure organizations from those that are just collecting more findings.
The post Finding vulnerabilities was never the hard part appeared first on CyberScoop.
This article explains why many breaches are driven by gaps in visibility rather than advanced exploits, how attackers move through modern environments, and what changes when organizations start connecting assets, identities, and attack paths into a single view.
A visibility problem exists when security teams cannot clearly answer three basic questions: what assets exist, who or what can access them, and how those elements connect. When those answers are incomplete, decisions are made based on assumptions – and that creates conditions where risk can grow, unnoticed.
As environments expand across cloud, SaaS, and hybrid infrastructure, the number of systems and identities grows quickly. What often falls behind is a clear understanding of how they relate to each other, and that gap is where attackers tend to operate.
A large medical technology organization experienced a breach driven by a series of compounding gaps rather than a single exploit. Internet-exposed assets created the initial entry point, while inconsistencies in device posture and identity enforcement, including gaps in platforms like Intune, weakened the security boundary. Attackers leveraged exposed or reused credentials and over-permissioned access to move laterally across systems. Without unified visibility across assets, identities, and managed devices, the attack path remained invisible until critical systems were reached.
Each of these conditions is common on its own, but what makes them dangerous is how they connect.
This breach did not rely on a zero-day vulnerability or an advanced technique. It depended on an exposed asset, valid credentials, and inconsistent enforcement across identity and devices. Those elements exist in most environments, but without visibility into how they overlap, they can be combined into a viable attack path.
Security teams often evaluate vulnerabilities individually, while attackers focus on how those weaknesses can be chained together. The risk is not just in what is vulnerable, but in how exposure allows movement.
Improving outcomes depends on understanding how exposure exists across the environment and how different elements relate to each other.
Asset visibility is the starting point. Many organizations cannot confidently identify everything that is externally accessible, and attackers often find assets that were never intended to be exposed. Continuously mapping assets across cloud and on-prem environments reduces that uncertainty and limits entry points.
Identity is just as critical. Once access is established, movement depends on credentials and permissions. Stolen credentials, over-permissioned accounts, and weak authentication paths allow attackers to move beyond initial entry. Treating identity exposure as part of the attack surface helps identify these risks earlier, especially when leaked credentials can be tied to active accounts and privileges.
Attack path visibility connects these elements. Instead of evaluating findings in isolation, it shows how exposures can be combined into realistic attack scenarios. Through adversarial simulation, organizations can observe how an attacker could move from an exposed system to internal resources, which shifts focus toward removing viable paths rather than addressing isolated issues.
External signals, such as credential leaks, only become meaningful when tied back to internal systems. Monitoring for exposed credentials is useful, but correlating those credentials with active accounts and access levels is what turns that signal into something actionable.
Controls such as least privilege and multi-factor authentication remain essential, but they are only effective when applied consistently. Without visibility into where access exists, enforcement gaps are difficult to detect.
The difference in a scenario like this is not simply better tooling. It is a shift in how exposure is understood and prioritized.
Attackers look for the easiest path through an environment. A visibility-first approach identifies those paths earlier, reduces them, and then examines why they existed. That changes how teams prioritize work, moving from reacting to individual findings toward removing viable attack paths.
This is where platforms like Rapid7 support a more complete view of exposure. Surface Command aggregates telemetry from over 190 sources, helping organizations unify fragmented views of assets and identities. InsightCloudSec extends that visibility into cloud environments by enforcing best practices and least privilege without relying on manual processes. Vector Command focuses on how attackers move, using continuous testing and simulation to show how attacks would unfold across an environment.
On the intelligence side, integrating threat data with identity systems allows external signals, such as credential leaks, to be mapped to active accounts and validated in real time. That makes it possible to act before those credentials are used.
Together, these capabilities provide a clearer understanding of how exposure translates into risk.
Zero trust depends on more than policy. It requires visibility, identity, validation, and enforcement to work together continuously.
Without visibility, zero trust becomes difficult to apply in practice. With it, security decisions can be based on how systems actually behave rather than how they are expected to behave, which shifts organizations away from reacting to incidents and toward preventing them from forming.
For years, cybersecurity professionals have relied on a familiar metric to dictate their day-to-day priorities: the Common Vulnerability Scoring System (CVSS). In today’s hyper-connected, sprawling IT environments, utilizing a static severity score as the ultimate arbiter of risk creates opportunities for threat actors. While defenders chase down theoretical, high-scoring alerts, adversaries are quietly targeting the truly exploitable, business-critical exposures that slip through the cracks.
In a recent report, Gartner® highlighted a projection:
"By 2028, organizations that prioritize exposures using threat intelligence, asset context, exploitability modeling and security control validation will reduce breach likelihood by at least 70% compared to peers relying primarily on CVSS-based vulnerability prioritization." [1]
This affirms what many seasoned practitioners have suspected for years: there’s an abundance of vulnerability findings, but a lack of actionable context.
Most vulnerability management programs evolved during a time when the attack surface was relatively static, adversary tooling was rudimentary, and remediation capacity generally exceeded the volume of new disclosures. Today, enterprises are confronted with vulnerabilities scattered across complex cloud architectures, SaaS applications, and intricate supply chains.
In this modern threat landscape, CVSS alone is insufficient because it measures theoretical severity, does not factor in whether an attacker is actually using the vulnerability in the wild, or consider the business value of any affected assets. According to Gartner®, fewer than 10% of vulnerabilities are exploited, yet most are treated as urgent [1]. This all leads to prioritization paralysis, where security teams spend countless hours patching vulnerabilities that pose low material risk to the business. The legacy approach rewards what is auditable rather than what is genuinely impactful.
To break free from endless patching and ineffective risk reduction practices, security professionals are shifting toward a context-driven model. As Gartner notes, strong exposure prioritization requires integrating four critical elements: threat intelligence, asset context, data science, and security control validation. Organizations are approaching these elements in a few practical ways:
Instead of just asking how severe a vulnerability is, modern exposure management asks whether an exposure is relevant to a threat actor who is capable of exploiting it right now. By embedding threat intelligence into each vulnerability finding, teams shift the focus from theoretical to risk active exploitation. It introduces the adversary's perspective by identifying known exploited vulnerabilities, public or private exploit availability, and targeted campaigns. By filtering out exposures with no evidence of attacker interest, organizations can instantly collapse large vulnerability backlogs and focus only on relevant threats.
Not all assets are created equal. A critical vulnerability on an isolated, internal test server is vastly different from the same vulnerability on a public-facing cloud workload processing customer sensitive data. Asset context enriches exposure data with crucial business information: what the asset is, its external accessibility, and its relationship to core business functions. Without this context, security teams waste disproportionate effort on low-impact systems, treating every critical alert as an equal emergency.
Security analysts often struggle to assess exploitability given the overwhelming volume of vulnerabilities. By using predictive models like the Exploit Prediction Scoring System (EPSS), organizations can analyze large datasets of historical exploitation to identify latent risks. Exposure assessment platforms should display this data alongside each exposure finding to make it easier to predict the vulnerabilities most likely to become attacks.
An exposure that appears highly exploitable in theory might be neutralized by existing defenses. By integrating security and policy controls, you can evaluate exposures in the context of endpoint protection and identity management. This passive validation confirms whether an attacker can realistically exploit the exposure in your specific environment.
Individually, each element highlighted above provides incremental value, but when integrated, they fundamentally transform how prioritization decisions are made. This integrated model ensures that remediation efforts are mobilized only after priorities have been validated in the context of the business and the current threat landscape. It transitions vulnerability management from a purely technical, tool-centric exercise into a strategic, process-driven risk decision.
Security leaders must measure success not by the sheer number of vulnerabilities closed, but by the demonstrable reduction of exploitable exposures and the alignment of remediation efforts with actual attacker behavior. Operationalizing these four elements requires a unified platform that eliminates the silos between vulnerability management, cloud security, and threat intelligence. You cannot manually stitch together disconnected spreadsheets and hope to outpace modern adversaries. This is where forward-thinking organizations are leaning on comprehensive, end-to-end solutions like Rapid7 Exposure Command that seamlessly aggregate visibility across on-premises and dynamic cloud environments. With deep, native integration of Rapid7 Cloud Security capabilities, teams can instantly map asset criticality and external accessibility within complex, ephemeral cloud architectures. Furthermore, by infusing world-class threat intelligence and active exploit data directly into exposure findings, Rapid7 enables security teams to cut through the noise, validate security controls, and pinpoint the exact exposures that matter most—all with minimal friction.
[1] Gartner, Prioritize What Attackers Will Exploit: 4 Elements of Strong Exposure Prioritization, Jonathan Nunez, 5 March 2026.
Engaging with the C-suite is not just about addressing security concerns or defending budget requests. It's about establishing and maintaining an ongoing discussion that aims to align security objectives with the interests of the business.
The post Communicating Security to the C-Suite: A Strategic Approach appeared first on Black Hills Information Security, Inc..
Risk is real. To better understand cybersecurity risk, let’s compare cyber risks to risks in the natural world from hurricanes. We can learn lessons from hurricanes and unnamed storms in […]
The post Cyber Risk Lessons We Can Learn From Hurricane Preparedness appeared first on Black Hills Information Security, Inc..
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