Russ Kirby, CISO at Ping Identity, shares how passion, courage, and “good enough” thinking shaped his path from HP to the C-suite—and what keeps him up at night.
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He replaces Guy Rosen, who announced his retirement from the company after 13 years.
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AI-generated code is part of everyday software development. Developers use it to prototype, refactor, troubleshoot, and move from idea to implementation with less friction than ever before. The productivity gains are undeniable, which means that security leaders now face a hard question: whether their organizations can govern the risk that AI creates at that same speed.
That challenge is rooted in scale. AI changes how quickly software can be created, while many application security programs still depend on controls designed for a slower development model. When code generation accelerates beyond the capacity to review, test, and remediate issues, security debt accumulates faster.
That is the hidden cost of AI-assisted development. Risk now enters the enterprise at machine speed, while many organizations still manage it with human-scale processes. CISOs should govern AI-generated code as a high-risk input: tested automatically, checked for unsafe dependencies, remediated quickly, and blocked from production if it fails policy.
Application security has long been measured through discovery. Teams count vulnerabilities, categorize severity, report trends, and show whether the numbers are improving. Those questions still matter, but AI adds a more urgent metric: risk velocity. Security leaders need to know how quickly the organization creates new software risks and how quickly it can reduce or eliminate them.
AI changes the economics of security debt. A development team that produces significantly more code without a matching increase in security capacity will create more issues than it can reasonably review or fix. Even when AI-generated code is comparable to human-written code on a per-line basis, the total risk can rise because the volume of change is higher. The backlog grows, vulnerabilities persist, and security debt eventually constrains the business.
The failure modes are familiar. AI coding tools can reproduce insecure patterns found in training data, including weak input validation, unsafe authentication flows, insecure direct object references, hard-coded secrets, and vulnerable dependency choices. They can also miss the context that determines whether code is secure in a specific environment: authorization models, tenant boundaries, data sensitivity, production configurations, and how services interact in a real application.
There is also a human factor. Under the pressure of deadlines, developers may accept code that works without fully understanding how it does so. The result is misplaced confidence. Code compiles, tests pass, features ship, and hidden risk enters the system. Over time, the organization may lose sight of the security concerns that naturally arose during manual development.
The software supply chain adds another layer of risk. Modern applications are assembled from open-source components, frameworks, plugins, containers, APIs, and cloud services. AI coding tools can recommend outdated packages, vulnerable libraries, or nonexistent dependencies. Veracode’s 2025 GenAI Code Security report found that AI coding tools produce insecure code nearly half (45%) of the time. It may sound like an amusing hallucination until attackers register malicious packages with similar names and wait for developers or automated tools to pull them in. At that point, a coding shortcut becomes a supply chain exposure.
AI is already part of the development lifecycle, and its use will continue to expand. Security teams need a control model built for that reality.
The industry has spent more than a decade moving security earlier in the development lifecycle, improving visibility and helping teams catch issues sooner. Many organizations, however, moved findings closer to developers without also moving enough ownership, automation, and remediation capacity with them. Developers received more alerts, while security teams gained more visibility into risks they still struggled to reduce.
AI makes that operating gap more urgent. As software output increases, security cannot remain a checkpoint near the end of the process. It must become a continuous control system built into the way software is created, tested, approved, and deployed.
Secure-by-design in the AI era requires an engineering environment where unsafe choices are harder to make and easier to catch. Approved frameworks, secure defaults, reference architectures, dependency controls, automated testing, and policy enforcement should be embedded directly into developer workflows and CI/CD pipelines.
Remediation also must move closer to the point of creation. When a coding assistant introduces a vulnerable pattern, the ideal response is an inline fix that is proposed, validated, and governed as part of the normal development process. AI can help defenders here when it is connected to reliable security signals, policy context, and evidence from real testing. Counterintuitively, developers using AI to write code often don’t trust AI to automatically remediate code without human review. This takes one of the best ways to keep up with machine-speed created vulnerabilities and slows it down to human speed. An acceptable balance between risk and speed must be found.
CISOs should focus on governance, not just approving AI coding tools. Governance means tracking where AI-generated code enters your environment, documenting the policies and tests applied, recording what issues were found and fixed, and keeping proof of these decisions. This documentation becomes critical as AI-assisted development becomes standard. If vulnerable code reaches production, you’ll need to show that adequate controls were in place and risks were managed according to policy.
CISOs and engineering leaders should treat AI-generated code as untrusted until proven otherwise. They must require automated testing before release, enforce dependency controls, prioritize remediation based on exploitability and business impact, and measure success by the rate at which critical risk is reduced.
Additionally, boards and organizational policymakers should ask whether organizations can demonstrate that AI-assisted software is governed before it is deployed. The key evidence should include the policies applied, the tests performed, the vulnerabilities remediated, the risks accepted, and the approvals recorded. Today, many organizations can confidently track what their AI tools produce, but they cannot demonstrate how that output was secured, reviewed, and governed before reaching production. The industry is still working to close this gap.
AI is changing how quickly software risk moves through the enterprise. The organizations that succeed will make security move just as quickly by embedding governance, remediation, and proof directly into the software delivery pipeline.
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Tarah Wheeler is CISO at TPO Group, a firm that provides cybersecurity consultancy for high-stakes organizations. But despite this elevated position, her journey was far from typical.
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Martin brings experience from Coinbase, Palantir, Amazon, and the U.S. Army to lead Uber's cybersecurity and enterprise security organization.
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The average cyberattack costs for a small- or medium-size business is more than $250,000. The salary for a chief information security officer (CISO) is about the same, pulling in between $250,000 and $400,000, according to the annual 2026 CISO Report from Sophos and Cybersecurity Ventures. Small- and medium-size businesses (SMBs) know they cannot afford the salary, so they roll the dice, hoping they will not be attacked. This is a dangerous gamble that these businesses, which make up the backbone of the American economy, should not have to take. A virtual (vCISO) or fractional CISO (fCISO) can provide a practical solution.
As the American economy goes digital, SMBs now rely on the same building blocks as big enterprises — cloud services, payment systems, remote access, customer data, and other third-party vendors. But without senior cyber leadership, cybersecurity often becomes a patchwork of tools, checklists, insurance paperwork, and whatever guidance a vendor offers. That may get these companies through a questionnaire; it will not build real resilience. Nearly half, all reported cyber incidents, which is projected to cost the global economy $12.2 trillion annually by 2031, involve smaller firms.
The threat is growing in both size and sophistication. Adversaries are deploying AI to automate reconnaissance, develop malware, and run phishing campaigns at scale. This reduces the cost and skill needed to target smaller firms at volume. Adversaries are also collecting encrypted data with the intent to decrypt it later when they have access to large enough quantum computers. SMBs in defense, healthcare, and financial supply chains often hold sensitive credentials that provide access into larger enterprise environments, but most are not prepared to adopt quantum-resistant encryption.
SMBs generally understand they face cyber risk. The real gap is leadership: someone who can turn technical vulnerabilities into business decisions, set priorities, brief executives, prepare for audits, and hold vendors accountable. For more SMBs, hiring a full-time CISO is financially unrealistic.
A Virtual CISO provides remote, on-demand cybersecurity leadership and advice, typically supporting several organizations at the same time. A fractional CISO is a dedicated, part-time executive who is more deeply integrated into one organization’s governance, security planning, and day-to-day operations. Both models give smaller organizations access to senior-level cybersecurity expertise in a flexible, more affordable way than hiring a full-time CISO.
Washington should make it easier for SMBs to hire fractional cybersecurity leaders, because the private market is not closing this gap on its own. The Cybersecurity and Infrastructure Security Agency (CISA) and the Small Business Administration (SBA) could help by publishing buyer guidance: vetted criteria for evaluating providers, example scopes of work and deliverables, and real-world case studies that show SMB owners what a high-quality vCISO or fCISO engagement should look like.
Clear guidance matters because many smaller firms cannot easily tell the difference between true cybersecurity leadership and a tool reseller, compliance-only consultant, or a generic managed services contract. Any vetted provider criteria should emphasize proven experience building and running security programs, independence from vendor incentives and product quotas, and the ability to tie security investment to real business risk, not just a list of certifications. Model scopes of work should also spell out the basics every engagement should deliver: an initial risk assessment, a prioritized remediation roadmap, and simple metrics that show whether security is improving over time. Without clear buyer criteria, federal efforts could end up funding low-quality services that add cost and paperwork without making companies safer.
The National Institute for Standards and Technology (NIST) should recognize these CISO models in its SMB-focused Cybersecurity Framework guidance. That would help smaller firms turn the framework’s Govern, Identify, Protect, Detect, Respond, and Recover functions into a clear, accountable leadership structure. This would make these roles less abstract: the point is not merely providing advice, but taking executive-level ownership of risk priorities, vendor oversight, incident readiness, and communication with the owner or board.
Congress and the Treasury Department should consider targeted tax incentives or credits for qualified cybersecurity leadership services, tied to measurable risk-reduction outcomes. Eligible activities could include completing a risk assessment, building a incident response plan, conducting vendor security reviews, running employee training, and producing a remediation roadmap. SMBs often defer cybersecurity because every dollar competes with payroll, inventory, and growth. A targeted incentive would make security leadership easier to justify as a business investment rather than an optional add-on.
Federal acquisition officials should require contractors that handle sensitive government data to show it has executive-level cybersecurity oversight, whether it is full-time, virtual, or fractional, and should extend that expectation down to relevant subcontractors and suppliers. This is necessary because SMBs serve as entry points into defense, healthcare, financial, and critical infrastructure supply chains.
Finally, CISA and the SBA should support vCISO- and fractional-CISO-led workforce training. Employees improve security when training comes with leadership, regular reinforcement, and clear accountability, not just annual awareness training. The aim is not to turn every SMB into a Fortune 500 security shop. It should be to give smaller firms access to the leadership they need before the next incident forces the issue.
Georgianna Shea, who is a Doctor of Computer Science, is chief technologist at the Foundation for Defense of Democracies’ Center on Cyber and Technology Innovation and its Transformative Cyber Innovation Lab, where Cason Smith served as a summer 2025 intern. Cason is studying integrated information technology at the University of South Carolina.
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