Articles on AI governance and AI risk: inventorying AI systems, assessing models and agents, and mapping to NIST AI RMF, ISO/IEC 42001 and the EU AI Act with DigitalXForce AI TRiSCM.
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 »
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 »
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.
AI-Powered Risk Governance and Security Posture Management with DigitalXForce Read More »
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.
AI in Cybersecurity: Need to Balance Innovation with Security and Trust Read More »
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.
Artificial intelligence and human intelligence play different roles in security. This article compares them and explains where each belongs in a cybersecurity program.
Real or Human Intelligence (RI) Versus Artificial Intelligence (AI) Read More »
A risk-based assessment of generative AI, using ChatGPT as the example: what secure by design means for these systems and where the gaps are today.
Risk-based Assessment of Generative AI: Is ChatGPT Secure-by-Design? 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
