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An AI-native security review framework that turns an agentic coding assistant into a structured security auditor for cloud-native applications, GenAI systems, and multi-cloud workloads. The framework is delivered as versioned steering documents and operating-model definitions that constrain the AI to evidence-grounded, verification-tiered output, eliminating the hallucinated findings that block enterprise adoption of off-the-shelf LLM security tools.

It combines four traditionally siloed activities under one operating model: full-project security review covering code, IaC, CI/CD, and GenAI/agent logic, with multi-repo IaC aggregationthat stitches infrastructure-as-code spread across multiple GitHub repositories into a single review context (so cross-repo interactions like a shared IAM role or a shared CI/CD module that misconfigures its consumers are caught in one pass); pentest scoping pack generation that produces vendor-ready, eight-document deliverables; CSPM-augmented verification that cross-references code findings against deployed cloud state through Model Context Protocol integrations with platforms like the customer's CSPM platform, cloud-native security services, and native cloud APIs; and documentation accuracy review via Confluence MCP that compares the customer's architecture and design docs against actual code, IaC, and deployed cloud state, so drift between documented intent and implemented reality surfaces as findings. The CSPM integration is bidirectional: the framework reads cloud signals for verification and writes its own findings back into the customer's CSPM platform tagged to the project, using the customer's own platform credentials.

The framework is cloud-agnostic by design. The same operating model runs across AWS, Microsoft Azure, and Google Cloud: IAM and OIDC trust analysis, cloud-native posture cross-reference, alignment to each provider's security framework, CIS Benchmark mapping, and the same Model Context Protocol integration pattern regardless of provider. Customers running multi-cloud workloads get one operating model, one finding taxonomy, and one consolidated risk register rather than a different tool per cloud.

Every finding carries a verification class, confidence score, file:line evidence, and exploit scenario, with severity capped by verification tier so Critical findings always require multi-source confirmation. The result is AI-assisted security work whose output can be defended in audit, handed to a pentester as scoping input, triaged in the customer's existing CSPM workflow, or used directly to drive remediation tickets.