Articles and practical perspectives from Jaiz Anuar on AI Security, cybersecurity, governance, and digital trust.
Part 2 turns AI security design principles into an operating model for production monitoring, prompt-injection boundaries, approval, survivable failure, supply-chain control and lifecycle governance.
Read article →A real AI sandbox uses default-deny egress, controlled DNS, allowlisted targets, synthetic credentials, independent kill controls and audit evidence—not behavioural instructions alone.
Read article →A practical framework for governing enterprise AI through explicit trust relationships, scoped machine identities, controlled data access, AI gateways, tool permissions and enforceable boundaries.
Read article →AI agents cannot accept risk or legal accountability. High-risk evaluations need enforceable isolation, real-time monitoring, independent shutdown controls and named incident owners before testing begins.
Read article →Safety guardrails tell an AI agent what it should do. Security boundaries determine what it can reach, access and execute when those guardrails fail.
Read article →AI agents are becoming non-human privileged identities. Their authority must be governed as carefully as any privileged user.
Read article →As attackers automate reconnaissance and campaigns with AI, deception must evolve into high-confidence trust validation.
Read article →Shadow AI is spreading through everyday work faster than governance can keep up. Visibility must come before control.
Read article →AI adoption is moving at employee speed while governance moves at organisational speed. The first challenge is visibility.
Read article →AI coding agents are rapidly becoming part of developer workflows. The key security question is not how intelligent they are, but what they are allowed to access, execute, and change.
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