Industry Experts

Legacy systems, safety concerns, and critical infrastructure risks make OT vulnerability disclosure one of cybersecurity's most challenging balancing acts.

Moving from isolated, technical data to a continuous risk lifecycle can help organizations align security controls with actual business consequences.

As AI-generated code becomes commonplace, CISOs need new audit strategies to measure developer practices, govern AI tool usage, and identify software risks before they reach production.

From model selection and automation to validation and measurable results, the right questions can help enterprises separate genuine AI capabilities from marketing hype.

As cybersecurity platforms embrace agentic AI, organizations must balance detection performance against the escalating costs of token consumption, deployment architecture, and AI credits.

From hidden content injections to cognitive state poisoning, attackers are turning trusted data sources into traps for autonomous AI.

Groups like ShinyHunters are demonstrating that attackers do not necessarily need malware or zero-day exploits to cause massive damage.

Four decades of incident response experience suggest that exploits are often the symptom, not the root cause, of today’s cybersecurity failures.

Security teams need more than visibility into AI applications, they need a repeatable framework for monitoring, investigating, and defending them in production.

AI-driven development is not something organizations can or should block. But it must be governed.

AI can help attackers generate malware, create malicious payloads, bypass simple security checks, and convert vague malicious intent into functional code.

CISOs are now facing machine-speed attacks and asking, “How do I agent?” The industry must provide remediation at scale.

As enterprises rush AI projects into production, security teams are increasingly being forced into reactive mode.

The organizations best prepared to face disruption are those that align security, continuity and risk management around what the business cannot afford to lose.

For AI data centers, where the stakes are the highest and performance constraints are the tightest, security and performance are no longer a zero-sum game.

Many AI-first enterprises have already embraced sovereign architectures for general AI initiatives; cybersecurity—and the SOC—should be next.