AI Governance

Articles on AI governance: inventorying AI systems, assessing models and agents, and mapping them to NIST AI RMF, ISO/IEC 42001 and the EU AI Act.

What Is AI Governance, and How Do You Run It Continuously?

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.

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Is It Safe to Use ChatGPT at Work? A Workplace Policy for Public AI Tools

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.

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Will AI Replace Cybersecurity Teams? What the Evidence Says

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.

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Why Continuous AI Governance Requires Technical Controls

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.

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How to Run AI Trust, Risk and Security Management in Operation

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.

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Gartner’s AI Trust, Risk and Security Management and TRiSCM, and What the C Adds

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.

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Streamlining AI-Powered Security Risk Governance and Management with DigitalXForce blog post banner

AI-Powered Risk Governance and Security Posture Management with DigitalXForce

This article explains how DigitalXForce runs AI-powered risk governance and security posture management together: automated assessments, one risk register, and Digital Trust as a shared measure.

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AI in Cybersecurity: Need to Balance Innovation with Security & Trust

AI in Cybersecurity: Need to Balance Innovation with Security and Trust

AI in cybersecurity raises speed on both sides. This article sets out how to balance innovation with security and trust: governance of models, evidence of controls and clear accountability.

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managing cyber risks

Managing Cyber Risks with Responsible AI

Generative AI brings new cyber risks alongside its benefits. This article explains how to manage them responsibly: governance, data controls, testing and continuous monitoring of AI systems.

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Illustration from the DigitalXForce article "Real or Human Intelligence (RI) Versus Artificial Intelligence (AI)"

Real or Human Intelligence (RI) Versus Artificial Intelligence (AI)

Artificial intelligence and human intelligence play different roles in security. This article compares them and explains where each belongs in a cybersecurity program.

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