VeriIR-Agent: Risk-Aware and Auditable Planning for LLM-Based Incident Response

Large language models can generate remediation recommendations for cybersecurity incident response, but natural-language outputs are difficult to verify field by field and to audit trade-offs among recovery benefits, execution risks, and business impacts. This paper proposes VERIIR-AGENT, which decomposes response planning into three separately evaluable layers: generation, selection, and verification quality. For generation, typed action records replace free-form responses, followed by deterministic model-independent checks of evidence, commands, policies, completeness, and attack paths. For selection, a multi-objective ranking method considers recovery benefits, evidence support, execution risks, business impacts, and rollback capability over a fixed candidate pool. For verification, a conditional framework decomposes safety guarantees into “coverage gaps” and “false passes on covered properties,” corresponding to checker scope and precision. Experiments use 2,219 incident-response cases while holding the model, test cases, retrieval context, and standard decoding parameters constant, comparing free-form generation, prompt-based structuring, and schema-constrained decoding. Schema constraints increase schema validity from 46.2% to 98.0% and completeness from 61.8% to 90.8%, while action-type matching increases only from 72.7% to 75.1%. On 4,438 candidate actions, risk-aware ranking reduces unsafe selection from 17.6% to 10.9% and high-impact selection from 43.8% to 35.7%, while increasing rollback coverage from 21.3% to 31.8%; recovery relevance decreases from 83.1% to 80.2% as the associated cost. These results show that structural compliance, selection preferences, and verification guarantees should be evaluated separately rather than collapsed into a single “reliability” metric, and none should be equated with successful real-world execution or production-grade safety.