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All Journal The Eastasouth Journal of Information System and Computer Science
Anil Mandloi
Independent Researcher, Phoenix, AZ, USA
Computer Science & IT

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Risk-Bounded Authorization and Runtime Verification for AI Agents Executing Sensitive Financial Operations Anil Mandloi
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1287

Abstract

AI agents are increasingly used to initiate or approve sensitive financial operations such as transfers, settlements, and limit changes. Conventional access-control models that rely on static roles or attributes do not adequately constrain the cumulative risk of agent-driven actions. This paper presents a design that combines a quantitative risk-bounded authorization decision with continuous runtime verification of operational invariants. Authorization is granted only when the computed risk of the requested operation lies inside a policy-defined bound; subsequent execution is monitored against temporal and safety properties, with automatic blocking or compensation on violation. Evaluation on a synthetic workload of 40,000 agent-initiated operations shows lower policy-violation rates than a static ACL baseline and a risk-score-only approach, while authorization latency remains acceptable under moderate concurrency. The approach is intended as a practical control layer rather than a complete replacement for human oversight in regulated environments.
Verifiable Transaction-State Reconciliation for AI-Orchestrated Financial Workflows Under Partial Failure and Duplicate Events Anil Mandloi
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i03.1290

Abstract

AI agents are increasingly tasked with orchestrating multi-step financial workflows that span internal services and external processors. These workflows are exposed to partial failures and duplicate events caused by retries and at-least-once messaging. This paper describes a reconciliation protocol that pairs a deterministic transaction state machine with idempotency controls, compensating actions, and a hash-chain ledger supporting Merkle proofs. On a synthetic workload of 50,000 workflow instances under controlled failure injection, the design reduced unresolved states and improved consistency relative to a simple retry baseline and a saga-only approach, at the cost of modest added latency. The resulting evidence chain is intended to support operational audit in regulated environments.
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