Policy-Aware Generative AI for Safe, Auditable Data Access Governance

• URL: http://arxiv.org/abs/2510.23474v1
• Date: Mon, 27 Oct 2025 16:10:55 GMT
• Title: Policy-Aware Generative AI for Safe, Auditable Data Access Governance
• Abstract summary: We present a policy aware controller that uses a large language model (LLM) to interpret natural language requests against written policies and metadata, not raw data.<n>The system, implemented with Google Gemini2.0 Flash, executes a six-stage reasoning framework with early hard policy gates and deny by default.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enterprises need access decisions that satisfy least privilege, comply with regulations, and remain auditable. We present a policy aware controller that uses a large language model (LLM) to interpret natural language requests against written policies and metadata, not raw data. The system, implemented with Google Gemini~2.0 Flash, executes a six-stage reasoning framework (context interpretation, user validation, data classification, business purpose test, compliance mapping, and risk synthesis) with early hard policy gates and deny by default. It returns APPROVE, DENY, CONDITIONAL together with cited controls and a machine readable rationale. We evaluate on fourteen canonical cases across seven scenario families using a privacy preserving benchmark. Results show Exact Decision Match improving from 10/14 to 13/14 (92.9\%) after applying policy gates, DENY recall rising to 1.00, False Approval Rate on must-deny families dropping to 0, and Functional Appropriateness and Compliance Adherence at 14/14. Expert ratings of rationale quality are high, and median latency is under one minute. These findings indicate that policy constrained LLM reasoning, combined with explicit gates and audit trails, can translate human readable policies into safe, compliant, and traceable machine decisions.

Related papers

• PolicyMem: Geometric Policy Memory for LLM Governance [72.04962480384322]
  We introduce PolicyMem, a geometric policy memory that externalizes natural-language policies as reusable geometric memory objects.<n>A memory writer compiles natural-language policies into policy memory slots, and query-response pairs read the policy memory through projection energy.<n>The resulting policy-evidence profile directly mediates the safety verdict and is reused for policy attribution and post-intervention verification.
  arXiv  Detail & Related papers  (2026-09-12T06:07:43Z)
• PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails [5.448131579126687]
  Policy prompting and LLM-as-a-judge pipelines often overlap the tasks of semantic grounding and policy reasoning.<n>To separate grounding and reasoning roles, we propose PL-Guard, a neurosymbolic guardrail architecture.<n>On the XSTest benchmark, an offline Qwen-based evaluator finds that PL-Guard with a hand-curated policy reduces unsafe compliance from 22.0% for the base model to 0.5%.
  arXiv  Detail & Related papers  (2026-08-16T10:58:19Z)
• Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI [0.6138671548064355]
  We formalize evaluation as policy-grounded correctness and introduce the Defensibility Index (DI) and Ambiguity Index (AI)<n>We validate the framework on 193,000+ Reddit moderation decisions across multiple communities and evaluation cohorts.
  arXiv  Detail & Related papers  (2026-04-22T18:05:29Z)
• PolicyBank: Evolving Policy Understanding for LLM Agents [51.86716874651299]
  PolicyBank maintains structured, tool-level policy insights and iteratively refines them.<n>PolicyBank closes up to 82% of the gap toward a human oracle.
  arXiv  Detail & Related papers  (2026-04-16T20:29:30Z)
• Policy-Invisible Violations in LLM-Based Agents [10.660248467840821]
  We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state, or session history absent from the agent's visible context.<n>We present PhantomPolicy, a benchmark spanning eight violation categories with balanced violation and safe-control cases.<n>We introduce Sentinel, an enforcement framework based on counterfactual graph simulation.
  arXiv  Detail & Related papers  (2026-04-14T01:15:15Z)
• Near-Miss: Latent Policy Failure Detection in Agentic Workflows [9.719140082591956]
  We introduce a novel metric for detecting latent policy failures in agent conversations traces.<n>Our results show that latent failures occur in 8-17% of trajectories involving mutating tool calls.
  arXiv  Detail & Related papers  (2026-03-31T12:26:35Z)
• Conformal Policy Control [50.46542384484142]
  We show how to use any safe reference policy as a probabilistic regulator for any optimized but untested policy.<n>Unlike conservative optimization methods, we do not assume the user has identified the correct model class.<n>Our experiments on applications ranging from natural language question answering to biomolecular engineering show that safe exploration is possible from the first moment of deployment.
  arXiv  Detail & Related papers  (2026-03-02T18:54:36Z)
• When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering [10.01278648231868]
  Policy steering is an emerging way to adapt robot behaviors at deployment-time.<n> Vision-Language Models (VLMs) are promising general-purpose verifiers due to their reasoning capabilities.<n>We propose uncertainty-aware policy steering (UPS), a framework that jointly reasons about semantic task uncertainty and low-level action feasibility.
  arXiv  Detail & Related papers  (2026-02-25T23:23:22Z)
• COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs [19.742967013586927]
  We present the first systematic framework for evaluating whether large language models comply with organizational allowlist and denylist policies.<n>We show that models reliably handle legitimate requests but catastrophically fail at enforcing prohibitions, refusing only 13-40% of adversarial denylist violations.
  arXiv  Detail & Related papers  (2026-01-05T06:57:45Z)
• Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces [12.671657542087624]
  Policy Reasoning Traces (PRT) is a form of specialized generated reasoning chains that serve as a reasoning bridge to improve an LLM's policy compliance assessment capabilities.<n>Our empirical evaluations demonstrate that the use of PRTs for both inference-time and training-time scenarios significantly enhances the performance of open-weight and commercial models.
  arXiv  Detail & Related papers  (2025-09-27T13:10:21Z)
• Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning [53.92712851223158]
  We formulate safety and privacy issues into contextualized compliance problems following the Contextual Integrity (CI) theory.<n>Under the CI framework, we align our model with three critical regulatory standards: EU AI Act, and HIPAA.<n>We employ reinforcement learning (RL) with a rule-based reward to incentivize contextual reasoning capabilities while enhancing compliance with safety and privacy norms.
  arXiv  Detail & Related papers  (2025-05-20T16:40:09Z)
• Few-shot Policy (de)composition in Conversational Question Answering [54.259440408606515]
  We propose a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting.<n>We show that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies.<n>We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning.
  arXiv  Detail & Related papers  (2025-01-20T08:40:15Z)
• PolicyLR: A Logic Representation For Privacy Policies [34.73520882451813]
  We propose PolicyLR, a new paradigm that offers a comprehensive machine-readable representation of privacy policies. PolicyLR converts privacy policies into a machine-readable format using valuations of atomic formulae. We demonstrate PolicyLR in three privacy tasks: Policy Compliance, Inconsistency Detection and Privacy Comparison Shopping.
  arXiv  Detail & Related papers  (2024-08-27T07:27:16Z)
• Conformal Off-Policy Evaluation in Markov Decision Processes [53.786439742572995]
  Reinforcement Learning aims at identifying and evaluating efficient control policies from data. Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty.
  arXiv  Detail & Related papers  (2023-04-05T16:45:11Z)
• Hallucinated Adversarial Control for Conservative Offline Policy Evaluation [64.94009515033984]
  We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, we seek to obtain a (tight) lower bound on a policy's performance. We introduce HAMBO, which builds on an uncertainty-aware learned model of the transition dynamics. We prove that the resulting COPE estimates are valid lower bounds, and, under regularity conditions, show their convergence to the true expected return.
  arXiv  Detail & Related papers  (2023-03-02T08:57:35Z)
• Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies [80.42316902296832]
  We study the estimation of policy value and gradient of a deterministic policy from off-policy data when actions are continuous. In this setting, standard importance sampling and doubly robust estimators for policy value and gradient fail because the density ratio does not exist. We propose several new doubly robust estimators based on different kernelization approaches.
  arXiv  Detail & Related papers  (2020-06-06T15:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.