Why OT Logging and Monitoring Are Executive-Level Controls

Detection speed determines impact.

OT environments are uniquely well-suited for behavioral monitoring due to their predictable patterns.

What CISOs Should Ensure

• Full visibility into remote access

• Alerting on configuration changes

• Monitoring of protocol violations

• Immediate alerts on break-glass usage

Executive Insight

Logs that are not operationalized do not reduce risk.

Final Thought: Visibility Is a Risk Multiplier

Undetected compromise extends dwell time, increases damage, and magnifies reputational impact. In OT environments, where change is infrequent and behavior is predictable, lack of detection is especially indefensible.

For CISOs, logging without alerting—or alerting without response—is performative security. Monitoring must exist to change outcomes, not to satisfy audits.

Generative AI Governance: Using the NIST Framework to Build Trust, Reduce Risk, and Lead Secure AI Adoption

Generative AI has moved faster than nearly any technology security leaders have dealt with. Tools that can generate text, code, images, and data insights are now embedded into productivity platforms, security tooling, development workflows, and business operations—often before security teams are formally involved. For CISOs, this creates a familiar but amplified challenge: innovation is happening faster than governance, and unmanaged generative AI introduces material risk across confidentiality, integrity, availability, compliance, and trust. For aspiring information security professionals, AI governance represents a growing and valuable discipline where strategic thinking matters just as much as technical depth. The good news? We don’t need to invent governance from scratch. NIST’s AI Risk Management Framework (AI RMF) provides a practical, flexible structure that security leaders can use today to govern generative AI responsibly and defensibly. Why Generative AI Governance Matt...

AI Governance Security Leadership | NIST AI RMF Series

A practitioner's deep dive into building a real generative AI governance program — from policy to controls to board reporting If you read my earlier post, Generative AI Governance: Using the NIST Framework to Build Trust, Reduce Risk, and Lead Secure AI Adoption , you got a solid introduction to why the NIST AI Risk Management Framework (AI RMF) matters and how its four core functions — Govern, Map, Measure, and Manage — provide a structure for responsible AI adoption. That post was intentionally high-level. This one is not. Over the past two-plus decades in security leadership, I have watched organizations repeatedly make the same mistake with emerging technology: they adopt first and govern later. We did it with cloud. We did it with mobile. We are doing it right now with generative AI — and the consequences are more significant than most leadership teams realize. Generative AI is not just another SaaS tool your employees are using without IT approval. It is a...

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