AI Security Architecture Is Not Just AI Governance
AI governance defines policies and responsibilities. AI security architecture turns them into technical controls, trust boundaries, identity models, evidence and tests.
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#Zero Trust
5 articles tagged Zero Trust on vesely.sk.
5 articles tagged Zero Trust on vesely.sk.
Threat Modeling for LLM Applications: Where to Start
LLM applications need threat models that cover more than prompts: trust boundaries, RAG, agents, tools, identity, data flows, provider boundaries and audit evidence.
Read article →Vibe Coding Can Build Your Application. But Who Builds Its Security?
AI can generate working applications quickly, but functionality is not the same as security. The next challenge is proving that AI-generated systems are hardened, compliant and trustworthy enough for production.
Read article →AI Just Changed the Threat Model
Autonomous AI agents change enterprise security assumptions. The next generation of cybersecurity will be about continuously proving that infrastructure is still trustworthy.
Read article →Risk Management Needs Evidence Infrastructure
NIS2, AI and modern infrastructure change risk management from a static register into a continuous evidence problem. Existing open-source tools solve parts of it, but the missing layer is proof of trust.
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