AI can make GRC decisions safely when the decision is repeatable, grounded in evidence and easy to reverse, and when a named person approves every decision that accepts risk, changes a control or will be relied on by someone else. Margareta Petrovic sets out where AI should and should not decide alone, how human review should work, how to explain an AI-assisted result to an auditor and how to keep confabulated output away from a decision.
AI governance is the set of policies, roles, controls and evidence an organization uses to decide which AI systems it runs, how they may be used and whether their controls keep working. This page sets out the lifecycle from inventory to retirement, maps it to the NIST AI RMF 1.0 and the EU AI Act, gives the EU AI Act dates as the European Commission states them after the AI Omnibus, and explains how to run AI governance continuously.
What Is AI Governance, and How Do You Run It Continuously? Read More »
Digital Trust translates connected assurance and risk evidence into an enterprise-level view for decision-makers. This page sets the definition beside the World Economic Forum’s and ISACA’s, separates Digital Trust from cybersecurity, security posture and compliance, shows which factors feed it, and states what evidence can measure and where any single score stops being useful.
Public AI tools such as ChatGPT and Gemini are safe enough for work when staff use approved business accounts, restricted data never goes into any tool and a person checks every output. This article compares provider terms by account type, gives a 6-part policy and maps each part to the NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
Is It Safe to Use ChatGPT at Work? A Workplace Policy for Public AI Tools Read More »
AI will not replace cybersecurity teams. It is taking over alert triage, evidence collection, control testing and first-draft reports, while people keep judgment, risk acceptance and accountability. Research published by ISC2, SANS, ISACA, Stanford and Carnegie Mellon in 2025 and 2026 shows the work moving from producing to checking.
Will AI Replace Cybersecurity Teams? What the Evidence Says Read More »
Rewrite each AI policy statement as a condition a system can check, with evidence, an owner and a frequency, so Continuous Control Monitoring can test it.
From AI Governance Policy to Continuous Control Monitoring Read More »
A policy states intent, and only a tested control shows what an AI system did this week. See why continuous AI governance needs technical controls and evidence.
Why Continuous AI Governance Requires Technical Controls Read More »
Run AI governance in operation: name an owner, inventory every AI system, write guardrails as tested controls, map them once and report the trend with evidence.
How to Run AI Trust, Risk and Security Management in Operation Read More »
Gartner names a discipline for governing AI systems. TRiSCM covers every control in the enterprise and adds Compliance as the fourth part. See the difference.
Gartner’s AI Trust, Risk and Security Management and TRiSCM, and What the C Adds Read More »
TRiSCM means Trust, Risk, Security and Compliance Management. See how one failed access review becomes four answers, and why one control record settles them.
What Is TRiSCM? The Answer, Told Through One Failed Control Read More »
Era of Automated Security Risk and Posture Management
Era of Automated Third Party Risk Management: Trust but Verify
Era of Automated Security Plans, Standards, Policies and Procedures
