Deeploy's AI Governance & Control Framework whitepaper is one of the most practical guides we've seen on operationalizing EU AI Act compliance. Here's what it means for enterprises deploying autonomous AI agents, and why identity is where governance has to start.
Deeploy's latest whitepaper, co-authored with Deloitte, BearingPoint, and a coalition of leading European consultancies, is a rigorous, practical roadmap for AI Act compliance. For enterprise teams thinking about agentic AI, one thread runs through every chapter: you cannot govern what you cannot identify.
From graveyard to jungle
Five years ago, most enterprise AI sat on a shelf. Today it's everywhere. Customer service, HR screening, financial decisioning, developer tooling. Thousands of employees building their own agents and workflows, often without oversight, documentation, or accountability.
The EU AI Act changes the stakes. For high-risk systems, and increasingly agentic AI qualifies, the obligations are concrete: risk management systems, data governance, transparency measures, human oversight, and full lifecycle documentation. Non-compliance carries fines of up to €35M or 7% of global revenue. The majority of rules come into force on August 2, 2026. The window to build compliant infrastructure is closing.
What the framework covers
The whitepaper organizes compliance into seven control categories covering 32 specific controls, with each section grounded in concrete evidence requirements and real-world failure cases. The categories span governance operations, risk management, data governance, transparency, human oversight, operations, and lifecycle management.
The business case is compelling. Organizations with mature AI governance grow 2 to 3x faster, see a 40 to 60% reduction in AI project failures, and get to market faster through defined approval processes.
Governance starts with identity
The whitepaper's Risk Management chapter opens with a requirement that sounds simple: maintain a complete, up-to-date registry of every AI system in your organization. In practice, most enterprises can't answer that question in five minutes.
With agentic AI, the challenge compounds. Agents act autonomously, access sensitive systems, and make decisions at machine speed. A registry of systems isn't enough. You need verified, persistent identity at the agent level.
That's the gap Insygna is built to close. Before you can govern an AI agent, monitor it, audit it, or demonstrate compliance to a regulator, it needs to be credentialed. Its identity, permissions, and behavioral record need to exist in infrastructure that enterprise systems can trust.
The Deeploy framework describes what good AI governance looks like at the organizational level. Insygna provides the identity layer that makes it enforceable at the agent level.