EpistemIQ
EpistemIQ
AI Governance Architecture for Regulated Industries
The Challenge
```Most AI implementations in regulated environments encounter audit failures 6-12 months after launch. Not because of poor execution, but because of architectural decisions made before implementation that guaranteed regulatory problems later.
Organizations deploy AI systems that appear to work, pass internal reviews, and satisfy vendors' promises, then fail when regulators require evidence chains the architecture can't provide.
The gap: technical teams build what AI can do, compliance teams audit against existing frameworks, and nobody identifies the structural incompatibility until it's expensive to fix.
```What I Provide
```I review AI governance architectures before implementation to identify structural issues that will create regulatory problems. This prevents expensive failures and rework.
The assessment identifies where systems are likely to fail regulatory scrutiny: not because of compliance gaps you can fix with better documentation, but because of architectural choices that conflict with regulatory requirements.
You get specific guidance on what to change before you build, not a list of problems to fix after deployment.
```What I Identify That Others Miss
```When I review AI implementations in regulated environments, I identify three categories of structural issues most consultants miss:
Architectural incompatibility: Where organizations are configuring LLM capabilities within compliance frameworks that assume invertible processes. The approach can't meet audit requirements, not because of poor implementation, but because of information-theoretic constraints that weren't considered during architecture decisions.
Misapplied governance structures: Where teams are deploying AI for appropriate use cases, but within oversight frameworks that prevent the technology from functioning as designed. The technology works, the governance works, but the combination creates systematic failures neither team anticipated.
Hidden compliance gaps: Where systems appear to meet requirements but are compressing away information that auditors will eventually demand. These gaps only become visible when you understand both how LLMs actually process information and what regulatory frameworks actually verify.
This diagnostic capability comes from understanding information theory, LLM architecture, and regulatory constraints simultaneously: a combination that allows me to see structural problems before they become expensive failures.
```Where This Matters
```This approach emerged from 20 years in life sciences research and compliance, where I've seen organizations repeatedly implement systems that can't survive regulatory scrutiny, not because teams lacked expertise, but because the architectural constraints weren't visible until too late.
Life sciences: FDA/EMA submission workflows, clinical trial oversight, post-market surveillance where AI-assisted decisions require evidence chains the current architecture can't provide.
Financial services: Model risk management frameworks, algorithmic trading oversight, regulatory reporting where systems generate required outputs but can't document the reasoning process auditors demand.
Healthcare systems: Clinical decision support, diagnostic AI implementations where patient safety requirements conflict with how the AI system actually processes information.
Research institutions: IRB processes reviewing AI tools, cross-departmental governance where technical and compliance teams can't effectively communicate about system capabilities and constraints.
```Why This Approach Works
```Regulatory frameworks (NIST AI RMF, EU AI Act, FDA guidance) identify what needs to be auditable. They don't address the architectural question: how do you create audit trails when AI systems compress information in non-invertible ways?
Most implementations try to retrofit traceability onto systems that weren't designed for it. This creates documentation that satisfies internal reviews but fails external audits.
The alternative: architect governance systems that account for information-theoretic constraints from the start. This requires understanding what's actually possible, not what vendors promise or what existing frameworks assume.
I help organizations make architectural decisions that prevent audit failures rather than fixing them after implementation.
```How Engagements Work
```Architecture review: Assessment of current or planned AI governance approach to identify structural issues that will create regulatory problems.
Gap analysis: Documentation of where the architecture conflicts with regulatory requirements and what changes prevent future failures.
Implementation guidance: Specific recommendations for architecting governance systems that survive regulatory scrutiny.
Engagements typically begin with a structured review to identify architectural risks, followed by detailed guidance on changes that prevent compliance failures.
```Background
```20+ years in research operations, risk, and compliance at Harvard University. Regulatory Affairs Certification candidate (Devices).
The theoretical foundation connecting biological persistence and machine learning alignment through informational compression is formalized in The Information-Theoretic Imperative: Compression and the Epistemic Foundations of Intelligence (arXiv:2510.25883).
EpistemIQ is patent pending. Implementation methodology is discussed under NDA.
```Work Together
```For architecture reviews, implementation guidance, or to discuss specific regulatory challenges:
Email: jenniferfkinne@proton.me
```