Why we're researching this
Enterprise AI is no longer a single application. Most organizations we work with now run some combination of Microsoft Copilot, an enterprise search platform, one or more custom RAG applications, internal AI tools, and increasingly, AI agents — each connected to some slice of the organization's knowledge. We're exploring what happens to authorization, governance and evidence once knowledge access is mediated by AI rather than a person browsing a folder structure directly, and we want to understand it from the security, governance and architecture leaders actually living with it, not assume we already know the answer.
The enterprise problem we're exploring
When a single team owned a single AI application, authorization was straightforward to reason about. That gets harder once:
- Multiple AI platforms and applications sit on top of the same underlying knowledge, each with its own permission model.
- Identity and group membership change continuously, but AI application permissions don't always update on the same clock.
- Connectors and custom RAG applications are added incrementally, often by different teams, without one shared authorization review.
- AI agents take multi-step actions across systems, not just answer single questions.
- Security, governance and audit teams are asked for evidence of correct AI behavior, often without a clear, consistent source for that evidence.
We're speaking with enterprise security and AI leaders to understand which of these matter most in practice, and where the real risk actually concentrates.
Questions we're exploring
- How do enterprises verify that AI authorization remains correct as identity and permissions change?
- How is AI access actually verified after a user's access is revoked?
- Who owns proving that AI authorization is working correctly — security, governance, platform engineering, or all three?
- How do enterprises verify authorization consistently across multiple AI platforms and applications?
- What evidence do security, governance and audit teams actually need — and in what form?
- How do enterprises test authorization behavior before deploying or changing an AI application, not just after an incident?
Current research hypotheses
These are the directions we're currently investigating. They're deliberately high-level, and none of them are validated yet.
Cross-AI Assurance
Exploring whether enterprises need independent verification of authorization across multiple AI platforms and applications, not just within one.
Authorization Change Assurance
Exploring whether authorization changes — new connectors, permission changes, revocations — require continuous regression testing and verification, not a one-time check.
Governance Evidence
Exploring whether security and governance teams need continuously generated evidence of AI authorization and policy behavior, rather than point-in-time attestations.
Knowledge Trust Secondary
A lower-priority thread: exploring whether enterprises also need stronger assurance around knowledge freshness, authority and provenance, alongside authorization.
Why this is harder than it looks
Most enterprise AI platforms are built to answer questions well, not to make authorization independently verifiable after the fact. Permission logic is typically embedded inside each platform's own connector framework and evaluated differently by each vendor, and it's rarely exposed in a form a security or audit team can inspect on its own terms. Add multiple platforms, frequent identity changes, and agents that take action rather than just answer, and confirming correct behavior becomes a standing verification problem — not a one-time configuration check done at rollout.
What AQEVON is exploring
AQEVON Enterprise Knowledge Fabric is an emerging product concept for connecting enterprise knowledge to AI while preserving authorization, provenance, context and governance — the same architecture principles behind our published Enterprise Knowledge Fabric reference architecture. AI Authorization Assurance is the specific research thread inside that initiative: independently verifying that AI authorization behaves as intended, across the platforms an enterprise actually runs. Both remain in research and design-partner discovery — neither is generally available today.
Enterprise AI Estate
Copilot, enterprise search, custom RAG applications, internal tools, AI agents.
Knowledge Sources
SharePoint, Confluence, GitHub, Jira, databases, documents — each with its own permission model.
Identity & Authorization
The enterprise identity provider's actual, current entitlements — not a static, separately maintained copy.
Verification & Governance Evidence
What we're researching: how this gets checked continuously, and what evidence it produces.
Who we want to hear from
We're prioritizing conversations with a small number of enterprise professionals working on this directly, including:
- CISOs and other security leaders
- AI governance leaders
- IAM leaders
- GRC / compliance leaders
- Enterprise architects
- AI platform engineering leaders
- Data and knowledge governance leaders
If any part of this matches something you're already thinking about — even informally, even without a settled view — we'd like to hear how you see it.