Contextualization of Third-Party Cloud Security Findings

Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although AI-native security assistants improve access to this data, they remain largely reactive: users must ask the right questions and interpret disconnected findings. This does not scale in enterprise environments, where signal importance depends on exposure, exploitability, dependencies, and business context. Repeated AI queries may therefore produce unstable prioritization without a structured basis for comparing assets. This paper introduces AI-native asset intelligence, a framework that transforms heterogeneous security data into a structured intelligence layer for consistent, contextual, and proactive asset-level reasoning. The framework combines a modeling layer, representing assets, identities, relationships, controls, attack vectors, and blast-radius patterns, with a scoring layer that converts fragmented signals into a normalized measure of asset importance. The scoring system separates intrinsic exposure, based on misconfigurations and attack-vector evidence, from contextual importance, based on anomaly, blast radius, business criticality, and data criticality. AI contextualization refines severity and business/data classifications, while deterministic aggregation preserves consistency. We evaluate the scoring system on a production snapshot with 131,625 resources across 15 vendors and 178 asset types. Sensitivity analyses and ablations show that severity mappings control finding sensitivity, AI severity adjustment refines prioritization, attack-vector scoring responds to rare exploitability evidence, and contextual modulation selectively modifies exposed resources based on business or data importance. The results support AI-native asset intelligence as a foundation for stable prioritization and proactive security-posture reasoning.

Cloud-security investigation is dominated by population tasks: which identities can read a data store, how many resources fail a control, what is reachable from another account. These resolve against a complete inventory, not a named object, so a partial answer to one is not a partial result but a different one. Coding agents can now be given read-only cloud credentials and asked to investigate directly, which raises the question of what a purpose-built security context layer still contributes. We evaluate the Sola Security Brain, a security intelligence layer whose relational substrate is resolved offline and whose security logic is evaluated against it at query time, against two coding agents operating the same live AWS environment through a read-only CLI, Claude Code and OpenAI Codex, over 28 investigation tasks. All three are scored by a blinded, tier-weighted, grounding-gated relative recall over one joint claim pool, so the scores share a denominator. The Sola Security Brain reaches 0.549 +/- 0.012 coverage against 0.340 +/- 0.011 for Claude Code and 0.281 +/- 0.006 for Codex, with the ordering identical in every grading draw. It leads 24 of 28 tasks from the cheapest model tier, at 17.8x and 20.8x lower cost per task than Claude Code and Codex. Beyond the aggregate, we describe an answer-level pattern we term sample-and-generalise: both agents enumerate a fraction of a large population, assert an unhedged universal negative, and disclose the sample size only in answer metadata rather than in the answer. In one task Claude Code reported that no bucket policies exist after checking four bucket families, in a sweep that sampled 40 of roughly 5,000 buckets, in an account where 65 buckets carry a wildcard-principal read grant.

IAM policy analysis typically emphasizes the authorization capabilities encoded in a policy, but security analyst review priority may also depend on the behavioral and environmental context surrounding a policy event. This paper evaluates whether contextual information provides measurable incremental value for interpretable IAM policy risk prioritization beyond policy and effective-authorization information. AWS is used as the experimental cloud provider because its IAM and audit-telemetry ecosystem enables controlled evaluation using AWS IAM Context Bench, a benchmark containing 534 real AWS experimental observations across policy, environment, and behavioral scenarios, including matched cases where policy and environment remain fixed while behavioral context changes. Three Explainable Boosting Machine models are evaluated under the same leakage-controlled grouped cross-validation protocol: a policy-centric baseline, a policy-plus-environment model, and a full-context model incorporating CloudTrail telemetry. The full-context model substantially reduces analyst-priority prediction error relative to the policy-centric baseline and closely tracks the reference priority ordering. In matched same-policy context pairs, the policy-centric model remains invariant, whereas the full-context model separates benign and suspicious behavioral conditions with high directional accuracy. The results also show improved concentration of high-priority cases at the top of simulated analyst review queues. These findings indicate that behavioral and environmental context can provide useful incremental information for analyst-oriented IAM risk prioritization while preserving an interpretable additive model structure. The formulation is applicable beyond AWS conceptually, although cross-provider validation remains future work.