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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#AI Security
AI security notes on LLM risk, secure AI-assisted development, AI threat modeling, AppSec and controls for trustworthy AI systems.
6 articles tagged AI Security 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 →Vibe Coding Needs Memory, Skills, and Review
Vibe coding is fast, but without durable memory, reusable skills, deterministic verification and independent review, AI-assisted development can quickly become chaos.
Read article →AI-Assisted Development Needs Security From Day One
AI makes it easier than ever to build and deploy software. That also means basic web security, AppSec and DevSecOps checks must happen before publishing, not after deployment.
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.
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