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Security researchers have flagged real vulnerabilities in the MCP ecosystem, but nearly all of them trace back to public, third-party servers. In the last paper of Aptive's Agentic AI Governance Series, Ian Meinert separates the risks that actually carry over to self-hosted MCP from the ones that don't, and lays out what federal contractors still owe their agencies as proof they got it right.
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Multi-model and multi-agent AI architectures are now the default way serious enterprise and federal AI work gets built. That power comes with real tradeoffs: expanded attack surface, harder audit trails and governance gaps most teams underestimate. Article 6 of Aptive’s Agentic AI Governance Series lays out the risk taxonomy, a control baseline mapped to NIST 800-53 and the architecture patterns that hold up in regulated environments.
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Multi-agent AI systems create an authorization problem most security frameworks were never built to address. Article 5 in Aptive's Agentic AI Governance Series introduces the Orchestrator Capability Manifest, a runtime-enforced policy document that governs which tools each AI agent can use, which prompt version it runs, which model it's pinned to and when a human has to review its output before it moves downstream.
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The first three articles in Aptive's agentic AI governance series defined the architecture. Article four defines the infrastructure that enforces it: container isolation, sandboxed code execution, zero trust network zones, CI/CD security gates and an AI incident response plan built around CISA's 72-hour reporting window.
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Most AI governance failures aren't technical. They're organizational. In Article 3 of Aptive's Agentic AI Governance series, Ian Meinert breaks down the nine artifacts that turn NIST AI RMF requirements into working governance: risk tolerance statements, accountability matrices, impact assessments, bias evaluation frameworks and more. If leadership hasn't defined what risk the organization accepts, who owns what and what happens when something goes wrong, the technical controls aren't enough.
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Passing an ATO review is not the same as staying compliant. This article maps the five gaps between a deployed multi-agent AI system and an operationally mature one: measurement, model risk, data governance, human oversight and continuous assurance, plus the evidence model auditors actually expect to see.
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Agentic AI systems make decisions across multiple steps with limited human review at each one. The NIST AI Risk Management Framework is the federal government's reference point for governing them, and it's the standard agentic AI programs will be measured against. This is article one of a seven-part series on what it takes to govern these systems well.
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Federal programs are deploying agentic AI faster than governance frameworks can follow. This seven-paper series, written from the practitioner's side, covers what actually has to work when multi-agent AI moves from pilot to production: security architecture, trust boundaries, governance artifacts, infrastructure and the evidence trail that gets a system through ATO.
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