Multi-Turn Jailbreaks and “Psychological Manipulation” Attacks: Why Single-Turn Defenses Fail

The AI doesn't break on the first question. It breaks on the tenth.The Conversation That Changed EverythingPicture this: A researcher sits at their terminal, chatting with one of the world's most advanced AI sys…...

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Policy-as-Code for AI Workloads in Kubernetes: Kyverno/OPA Patterns for Model and Data Safety

1. Why This MattersYour container is signed. Your image is scanned. Your CVE count is zero.None of that stops a backdoored model from running inference....

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Securing Agentic AI: Roadmap Part-10

Part 10. Implementation Roadmap10.0 Why you need a roadmap, not a random pile of botsYou now have:Agent patternsMulti agent topologiesHITL designsThreats and controlsIdentity, architecture, governanceGreat. Now the obvi…...

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Securing Agentic AI: Governance Framework Part-9

Part 9. Governance Framework9.0 Why you need actual governance, not “vibes”At small scale, you can ship an agent, watch it in Prod, and fix things as they break.At enterprise scale, that same approach turns into:Nobody …...

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Securing Agentic AI: Enterprise Integration Part-8

8. Enterprise Integration8.0 Why this part mattersUp to now we treated agents like a new thing. Your CISO, CIO, and Head of Architecture do not care about "new things". They care about one question: "How …...

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Securing Agentic AI: Secure Architecture Patterns Part-7

7. Secure Architecture Patterns7.0 Why architecture beats clever promptsHere is the uncomfortable truth: If your main security control is "We wrote a really strong system prompt", you will lose. Not today. May…...

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Securing Agentic AI: Identity and Access Control for Agents Part-6

6. Identity and Access Control for Agents6.0 Why identity is the real security boundaryFor classic apps, you already know the game:User authenticates.App runs with app identity.App hits databases and services with that …...

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Securing Agentic AI: Threat Landscape for Agentic Systems Part-5

5. Threat Landscape for Agentic Systems5.0 Why this part mattersWith normal LLM apps, a bad output is embarrassing.With agentic systems, a bad output can:Send money to the wrong placeDrop a serverEmail all your customer…...

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