Research
Operationalizing Algorithmic Equity
A socio-technical overlay framework for mapping NIST 800-53 security controls to the NIST AI Risk Management Framework (AI RMF 1.0).
Applied Doctorate in Cyber Security · East Bridge University, Paris · Program began April 2026 · Expected completion 2028
The problem
Equity needs controls, not just principles
The AI RMF tells organizations what trustworthy AI looks like. It leaves open how to implement and evidence it. Security teams already run mature control programs built on NIST 800-53, with owners, assessments, and audit trails.
The approach
A socio-technical overlay
The proposed research would develop an overlay that maps 800-53 controls to AI RMF outcomes, covering both the technical controls and the organizational and human practices that algorithmic equity depends on. It would follow design science research: build the artifact, then evaluate it.
Literature
Six research streams
01
NIST AI RMF implementations
How organizations have applied the AI Risk Management Framework in practice, and where the guidance stays abstract.
02
AI governance in regulated domains
Model risk management and consumer-protection expectations in financial services, and how controls differ across domains.
03
Algorithmic fairness auditing
How fairness is measured and audited, and what an auditor can verify with evidence.
04
NIST 800-53 extensions and overlays
How overlays tailor the 800-53 control catalog to a specific context, the pattern this research extends to AI.
05
EU AI Act and comparative governance
How the EU AI Act and ISO/IEC 42001 compare with U.S. frameworks, and what a shared control language could bridge.
06
Design science methodology
Patterns for building and validating a design artifact with rigor.
In practice
The platforms and the research
Enclave suite
Tools that map controls, document evidence, and score policies against NIST catalogs. The practical side of control mapping.
Regional compliance platforms
Ley81-Suite, TerraGuard-PT, and Borealis-Compliance apply comparable control logic in other jurisdictions.
Documentation platforms
DocAudit-AI, Sprint2Docs-AI, and related tools address the documents that evidence a governance program.
Working on similar problems?
I welcome conversations with practitioners, researchers, and organizations working on AI governance and control mapping.