Agentic AI Security Research
10 DOI-registered publications establishing why AI decisions require settlement.
These publications trace a single, unavoidable conclusion: without per-decision closure, accountability in agentic AI is structurally impossible.
This work does not propose compliance checklists or ethical guidelines. It demonstrates—step by step—why responsibility must be closed, not merely observed.
Foundations
Formal definition of responsibility completion (Δ1) for autonomous AI systems. Five-level leakage taxonomy. Binary closure conditions for per-decision accountability.
4 publications · Covers risk taxonomy, structural governance analysis, and the formal foundations of responsibility completion (Δ1).
Empirical Validation
Large-scale adversarial testing of AI defense mechanisms across frontier models. 18,232 API calls, six model families, three geographic regions, 24 test conditions.
3 publications · Covers adversarial evaluation of AI defense mechanisms across frontier models, including reproducibility protocols and multi-session evaluation methodology. Specific attack vectors and defense classes available under NDA.
Applied Research
Settlement infrastructure for autonomous financial operations and platform-scale governance. Pre-attestation protocols for irreversible agent transactions.
3 publications · Covers settlement protocols for irreversible agent transactions, infrastructure engineering for high-throughput environments, and platform-scale governance audit methodology. Implementation details available under NDA.
Policy Engagement
OIA Lab submitted a formal response to the NIST Request for Information on Agentic AI Security (NIST-2025-0035, March 2026), addressing 17 sub-questions across all 5 sections.
Independent Validation
Nicholas Caputo (University of Oxford) has independently published parallel research on AI decision accountability, arriving at structurally similar conclusions from different starting axioms.
Independent convergence from separate research groups strengthens the foundational claim: per-decision closure is not optional—it’s structurally required.
Regulatory Alignment
Our research maps directly to EU AI Act (Art. 9, 12, 13, 14), NIST AI RMF, ISO/IEC 42001:2023, SOC 2 evidence controls, PSD2/PSD3, Colorado AI Act, and OWASP Agentic AI Top 10.
See Compliance for detailed mapping.
What this research establishes
• Accountability cannot rely on observation alone
• Logging and scoring cannot define completion
• Governance without closure produces unbounded liability
• Responsibility finality must be engineered
These findings motivate a single engineering conclusion:
AI decisions require settlement.
Access
All publications are DOI-registered. Full papers, methodology, and datasets are available on request for research collaboration, due diligence, and technical evaluation.