Kumar Ritesh

expert panel

As companies move more critical systems and data into cloud and SaaS environments, investigating a cyber incident can become a complicated exercise in reconstructing events across systems an organization doesn’t fully control. If important evidence is unavailable or incomplete, determining what happened—and demonstrating what did or didn’t happen—can become much harder. NIST’s work on cloud forensics highlights the distinct challenges investigators face when collecting and analyzing evidence in cloud computing environments.Access to that evidence isn’t always a given. In 2024, CISA, OMB and ONCD worked with Microsoft to expand cloud audit logging for federal customers regardless of license tier and increase default retention from 90 to 180 days, part of a broader push to make critical security logs available without added licensing barriers. That highlights a larger business risk: An organization may discover the limits of its forensic visibility only after an incident, when legal teams, regulators and insurers are looking for clear answers.Cybersecurity leaders therefore need to look beyond whether their cloud environments can detect suspicious activity and determine whether those environments can support a credible investigation after a breach or other security incident. Members of the Senior Executive Cybersecurity Think Tank bring deep experience in enterprise security, incident response, risk management, regulatory compliance and cloud security. Below, they examine how leaders can strengthen forensic readiness before an incident occurs and where evidence gaps can create the greatest problems once an investigation is underway.

expert panel

Cybersecurity teams have always had to distinguish legitimate activity from malicious activity, but external AI agents make that judgment call harder. Unlike a conventional user session or scripted bot, an agent can pursue a goal across multiple steps, adapt as conditions change and operate at a speed and scale that can far exceed human activity.The challenge grows when organizations are interacting with agents they didn’t build, deploy or directly control. Those agents may be acting on behalf of customers, vendors or business partners—or probing systems for an attacker—and their behavior can evolve in real time. In a recent real-world example, AI agents run internally by OpenAI found an unintended path to the internet while trying to complete an evaluation, then breached Hugging Face systems in an effort to obtain information that could help them finish the task. The incident illustrates how quickly an autonomous system can move beyond the boundaries its operators expected.For cybersecurity leaders, that raises a broader question: How do you defend systems when the actor at the other end may be autonomous, adaptive and outside your control? Members of the Senior Executive Cybersecurity Think Tank bring deep expertise in enterprise cybersecurity, risk management, threat detection and modern security architecture. Below, they explore how defensive strategy should evolve as external AI agents become more capable and which protections matter most when agent-driven activity originates beyond the organization.

expert panel

Software supply chain security is often treated as a gatekeeping problem: Keep malicious code from entering the environment. But modern applications are built from dense webs of open-source and third-party dependencies, and a single compromised package can surface across numerous applications and systems downstream, far beyond the first project that installs it.Recent attacks have shown how quickly that risk can multiply. In September 2025, the self-replicating Shai-Hulud worm infiltrated the npm ecosystem through compromised maintainer accounts, spreading automatically across the registry by hijacking developer credentials. By the time it was contained, the worm had compromised more than 500 packages, prompting GitHub to remove them from the registry to stop further propagation.For security leaders, the challenge isn’t only determining whether a package is safe at the point of entry—it’s understanding how far a compromise could travel once that software is already embedded across an organization. Supply chain risk is compounded when organizations lack visibility into how the technology they rely on is developed, integrated and deployed.Members of the Senior Executive Cybersecurity Think Tank bring deep expertise in enterprise cybersecurity strategies, data breach prevention, risk management, threat detection and modern security architecture. Here, three of them examine how leaders should assess the downstream risk posed by poisoned software dependencies and what meaningful prevention and containment look like when a single compromised component has the potential to affect many systems.

expert panel

The technology businesses depend on every day also creates openings for attackers, and the vulnerability queue isn’t getting shorter. CVE submissions to NIST's National Vulnerability Database increased 263% between 2020 and 2025, and submissions in the first three months of 2026 were nearly one-third higher than during the same period a year earlier. For security teams, that volume makes treating every newly disclosed flaw as an isolated problem increasingly difficult—and risks turning vulnerability management into an endless cycle of finding, prioritizing and patching.Security leaders need to look beyond individual CVEs and address the recurring weaknesses that keep generating new vulnerabilities. Recent analysis of more than 39,000 CVE records highlights how identifying recurring root causes can inform security investments and development practices aimed at eliminating entire classes of defects. The challenge for security leaders is doing that longer-term work without losing sight of vulnerabilities that pose an immediate threat to the business.Members of the Senior Executive Cybersecurity Think Tank bring deep experience in enterprise cybersecurity strategy, risk management, threat detection, secure architecture and breach prevention. Below, three of them share how security leaders can balance urgent remediation with a more systemic approach to vulnerability management—reducing today’s risk while working to prevent the same kinds of flaws from resurfacing tomorrow.

expert panel

As AI agents move from assisting employees to taking actions on their behalf, organizations face a new accountability problem: It’s no longer enough to know what happened. As agents gain access to sensitive data, systems and business processes, organizations are likely to need evidence that establishes not only what an agent did but also why it was permitted to do it. That includes demonstrating that an agent had the right authority, was acting for the right purpose, and stayed within the boundaries established for that particular task.Cybersecurity experts have warned that agentic systems complicate the traditional model in which actions can be traced to an identifiable person with defined permissions and a clear audit trail. And at the moment, teams may need to work on solving the AI accountability conundrum on their own—while the issue is unquestionably on guiding agencies’ radars, NIST only began requesting input for a project on how identity standards and best practices can be applied to software agents in February 2026. Among those with an eye on the security challenges that come with AI agents are the members of the Senior Executive Cybersecurity Think Tank. They bring deep experience in enterprise cybersecurity, risk management, security architecture and emerging technology to bear on the question. Below, two of them examine how organizations should rethink evidence and auditability for agent-driven workflows and what stronger, more trustworthy proof of authorization could look like in practice.

expert panel

Cybersecurity has long been a race between attackers trying to get in and defenders trying to keep them out. But frontier AI is changing the pace of that race. By helping threat actors discover vulnerabilities, conduct reconnaissance and experiment with attack techniques more quickly and at a greater scale, AI can shrink the time security teams have to recognize a threat and respond before it causes damage. Offensive activities that once took weeks can increasingly be compressed into minutes. That shift also challenges a more fundamental assumption behind traditional detection: that defenders will recognize enough of an attack to know what they’re looking for. Frontier AI can help adversaries vary techniques, combine attack methods and explore unfamiliar paths at a scale that makes relying primarily on established signatures, indicators and playbooks increasingly risky. Cybersecurity guidance is consequently evolving toward models that account for AI-enabled attacks while also using AI to strengthen defense and proactively address emerging threats.Preparing for that environment means looking beyond how quickly an organization can identify an attack to how much room for maneuver its systems leave an attacker in the first place. Members of the Senior Executive Cybersecurity Think Tank share deep expertise in enterprise cybersecurity strategies, data breach prevention, risk management, threat detection and modern security architecture. Below, they explore how security leaders should adapt their defenses for an AI-accelerated threat landscape and which capabilities are becoming most important.

expert panel

NIST emphasizes that effective AI risk management depends on organizations being able to govern, map, measure and manage how AI systems are used. However, following that guidance is becoming increasingly complicated.Artificial intelligence is moving into everyday business operations faster than many organizations’ security and governance processes can keep up. Employees and business teams can now build workflows around large language models, add AI copilots to existing tools, and connect agents to company systems without the kind of formal deployment process that traditionally gives security teams visibility into new technology. As AI moves from standalone experimentation into copilots, connected workflows and increasingly autonomous agents, security teams may have less visibility into where it’s being used, what data it can reach and what actions it’s authorized to take. The result is a widening gap between the AI environment an organization believes it is governing and the one actually taking shape across the business.It’s essential for security leaders to gain visibility into the web of AI systems being built across their organizations—and the ever-expanding attack surface that comes with it. Below, members of the Senior Executive Cybersecurity Think Tank—with expertise spanning enterprise cybersecurity strategy, breach prevention, risk management and cybersecurity leadership—explain how security leaders can get ahead of business-led AI deployments and recognize when unmanaged AI risk is already beginning to compound.

expert panel

An attacker who gains access through a stolen credential may quickly move across devices, cloud resources and network systems in search of valuable data or greater control. Each step can generate clues, but when those signals land in separate tools and queues, security teams may struggle to recognize the full attack before the damage spreads.This challenge is becoming harder to tackle as traditional boundaries between users, devices, applications and infrastructure continue to dissolve. NIST’s zero-trust guidance reflects that shift, moving security away from static, network-based perimeters and toward continuous decisions based on users, assets and resources. This same erosion of boundaries is what makes it harder to catch attackers who don’t need to break anything to move around. CISA’s guidance on identifying and mitigating “living off the land” techniques warns that attackers can abuse legitimate, trusted tools and processes to blend in with normal system activity, making isolated alerts harder to interpret without broader context.Yet many organizations still measure security effectiveness largely by how well they detect suspicious activity. Detection remains essential, but alerts alone don’t determine which risks matter most, coordinate action across environments, or help the business continue operating when defenses fail. That requires an approach that connects visibility with timely decisions, enforceable controls and plans for maintaining and restoring critical operations.Moving beyond a detection-centric model means reconsidering how security data, decisions and defenses work together across the enterprise. Below, members of the Senior Executive Cybersecurity Think Tank—with expertise in enterprise cybersecurity strategies, data breach prevention, risk management, threat detection and cybersecurity leadership—explain what a more unified, real-time approach to visibility, control and resilience should look like.

expert panel

A vendor’s data breach doesn’t stay neatly contained within its own systems. Business clients may face exposed information, operational disruptions, regulatory scrutiny and difficult questions from their own stakeholders, even when their networks weren’t directly compromised. Third-party risk management is a growing issue for companies whose customers’ data resides in a growing web of applications and workflows.Once the initial crisis passes, vendors typically issue reassuring statements about investigations, remediation and stronger security. But communications crafted for customers and the media can’t establish whether the conditions that allowed the attack have truly been addressed. Telecommunications and media giant Comcast learned this the hard way in 2024 when a vendor initially said a breach hadn’t impacted Comcast’s customers—only to reverse course more than four months later.For organizations deciding whether to continue trusting a breached provider, the real question isn’t whether the vendor responded quickly or communicated effectively. It’s whether the vendor can demonstrate that it learned from the incident and has taken real, effective steps to reduce the risk of another one. Below, members of the Senior Executive Cybersecurity Think Tank—with expertise in enterprise cybersecurity strategy, data breach prevention and risk management—share what would convince them that a breached vendor will be a safer partner going forward.

expert panel

Companies rarely operate wholly within their own digital walls anymore. Critical business functions now depend on cloud platforms, software components, contractors, service providers and sprawling networks of suppliers, with each trusted relationship creating another possible route into an organization. And the risk is growing: Verizon’s 2026 Data Breach Investigations Report found that breaches involving third parties had increased 60% from the previous year’s dataset, accounting for 48% of all breaches analyzed.For attackers, compromising a well-connected partner can be faster and more effective than challenging each target’s defenses directly. A 2025 GitHub Action supply chain compromise demonstrated how a tool embedded in development workflows could expose credentials and other sensitive information across downstream users. Yet many organizations still evaluate cyber risk from outside partners primarily through questionnaires and scheduled reviews—methods that document security practices but often fail to reveal how risks, access or dependencies have changed since the assessment was completed.Reducing third-party cyber risk now requires organizations to look beyond whether a vendor passed an assessment and consider how trusted relationships affect their security and resilience over time. Below, members of the Senior Executive Cybersecurity Think Tank—with expertise in enterprise cybersecurity strategy, data breach prevention and risk management—explain what effective third-party risk reduction looks like today and where (and why) companies must move beyond checklist-driven oversight.

expert panel

Artificial intelligence has fundamentally changed cybersecurity—not just for defenders but also for attackers. AI can now generate convincing phishing campaigns, rapidly mutate malware and automate reconnaissance at a scale that would have required large teams only a few years ago. As attack velocity accelerates, many organizations are discovering that even mature detection and response capabilities struggle to keep pace.That shift is forcing security leaders to rethink longstanding assumptions. Members of the Senior Executive Cybersecurity Think Tank, a community of experienced cybersecurity executives and practitioners, argue that future-ready organizations will succeed not by responding faster, but by preventing more attacks from succeeding in the first place.According to IBM's Cost of a Data Breach Report, organizations that combine AI-driven security with proactive risk reduction significantly reduce both breach costs and response times, reinforcing the business value of moving security "left" before attackers gain a foothold.Below, Think Tank members share practical strategies for moving beyond reactive security, highlighting the technologies, processes and leadership mindset needed to stay ahead of these AI-powered threats.