AI Governance
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Agents
Copado puts deployment authority behind local MCP and explicit approval gates
Copado's Agentia Headless gives coding agents MCP and CLI access to delivery workflows while keeping approvals, quality gates and release state outside the model.
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AI Governance
Microsoft’s new AI code makes human control a behavior requirement
Microsoft’s draft Humanist AI code emphasizes corrigibility, shutdown and human control. The key question is whether the principles become auditable tests.
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Research
K-Bench finds agent unlearning can hide leaks from answer-only tests
K-Bench finds answer-only unlearning tests can miss secrets exposed through retrieval, tools and other agent execution channels.
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AI Governance
Anthropic plans permanent embedded AI safety evaluators with employee-like access
Anthropic says external AI safety evaluators will get ongoing employee-like access. The plan strengthens verification but still lacks a common audit standard.
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Business
OpenAI rules out a 2026 IPO, linking the delay to safety governance
Sam Altman says OpenAI will not go public in 2026. Aipolix examines what the delay changes for governance, safety decisions and transparency.
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Agents
RubyGems incident shows how constrained agents can turn package publishing into an execution path
The RubyGems campaign shows why agent security must model package publishing, build automation and other indirect write paths as part of the execution surface.
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Research
GuardedAct tests a safer boundary for AI-driven incident remediation
GuardedAct reports lower collateral damage when LLM-generated repairs pass through sandbox simulation and an independent execution gate.
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Research
RAG-Safety-Bench shows why model safety does not automatically survive retrieval
RAG-Safety-Bench finds that base-model safety does not reliably transfer to RAG systems and proposes four controlled retrieval conditions for evaluation.
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Research
EBL-Core separates an agent’s policy approval from its authority to execute
EBL-Core proposes revalidating action identity, policy, evidence and context when high-risk AI agents exercise execution authority, not only when approval is issued.
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🛑 AI Governance Fails Without Containment (6/10)
Why agent governance needs runtime containment: stop mechanisms, blast-radius controls, rollback, behavior canaries and recovery evidence.