aegisops-ai — Автоматизация аудита безопасности и затрат в разработке

--- name: aegisops-ai description: "Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance." risk: safe source: community author: Champbreed date_added: "2026-03-24" --- # /aegisops-ai — Autonomous Governance Orchestrator AegisOps-AI is a professional-grade "Living Pipeline" that integrates advanced AI reasoning directly into the SDLC. It acts as an intelligent gatekeeper for systems-level security, cloud infrastructure costs, and Kubernetes compliance. ## Goal To automate high-stakes security and financial audits by: 1. Identifying logic-based vulnerabilities (UAF, Stale State) in Linux Kernel patches. 2. Detecting massive "Silent Disaster" cost drifts in Terraform plans. 3. Translating natural language security intent into hardened K8s manifests. ## When to Use - **Kernel Patch Review:** Auditing raw C-based Git diffs for memory safety. - **Pre-Apply IaC Audit:** Analyzing `terraform plan` outputs to prevent bill spikes. - **Cluster Hardening:** Generating "Least Privilege" securityContexts for deployments. - **CI/CD Quality Gating:** Blocking non-compliant merges via GitHub Actions. ## When Not to Use - **Web App Logic:** Do not use for standard web vulnerabilities (XSS, SQLi); use dedicated SAST scanners. - **Non-C Memory Analysis:** The patch analyzer is optimized for C-logic; avoid using it for high-level languages like Python or JS. - **Direct Resource Mutation:** This is an *auditor*, not a deployment tool. It does not execute `terraform apply` or `kubectl apply`. - **Post-Mortem Analysis:** For analyzing *why* a previous AI session failed, use `/analyze-project` instead. --- ## 🤖 Generative AI Integration AegisOps-AI leverages the **Google GenAI SDK** to implement a "Reasoning Path" for autonomous security and financial audits: * **Neural Patch Analysis:** Performs semantic code reviews of Linux Kernel patches, moving beyond simple pattern matching to understand complex memory state logic. * **Intelligent Cost Synthesis:** Processes raw Terraform plan diffs through a financial reasoning model to detect high-risk resource escalations and "silent" fiscal drifts. * **Natural Language Policy Mapping:** Translates human security intent into syntactically correct, hardened Kubernetes `securityContext` configurations. ## 🧭 Core Modules ### 1. 🐧 Kernel Patch Reviewer (`patch_analyzer.py`) * **Problem:** Manual review of Linux Kernel memory safety is time-consuming and prone to human error. * **Solution:** Gemini 3 performs a "Deep Reasoning" audit on raw Git diffs to detect critical memory corruption vulnerabilities (UAF, Stale State) in seconds. * **Key Output:** `analysis_results.json` ### 2. 💰 FinOps & Cloud Auditor (`cost_auditor.py`) * **Problem:** Infrastructure-as-Code (IaC) changes can lead to accidental "Silent Disasters" and massive cloud bill spikes. * **Solution:** Analyzes `terraform plan` output to identify cost anomalies—such as accidental upgrades from `t3.micro` to high-performance GPU instances. * **Key Output:** `infrastructure_audit_report.json` ### 3. ☸️ K8s Policy Hardener (`k8s_policy_generator.py`) * **Problem:** Implementing "Least Privilege" security contexts in Kubernetes is complex and often neglected. * **Solution:** Translates natural language security requirements into production-ready, hardened YAML manifests (Read-only root FS, Non-root enforcement, etc.). * **Key Output:** `hardened_deployment.yaml` ## 🛠️ Setup & Environment ### 1. Clone the Repository ```bash git clone https://github.com/Champbreed/AegisOps-AI.git cd AegisOps-AI ``` ## 2. Setup ```bash python3 -m venv venv source venv/bin/activate pip install google-genai python-dotenv ``` ### 3. API Configuration Create a `.env` file in the root directory to securely store your credentials: ```bash echo "GEMINI_API_KEY='your_api_key_here'" > .env ``` ## 🏁 Operational Dashboard To execute the full suite of agents in sequence and generate all security reports: ```bash python3 main.py ``` ### Pattern: Over-Privileged Container * **Indicators:** `allowPrivilegeEscalation: true` or root user execution. * **Investigation:** Pass security intent (e.g., "non-root only") to the K8s Hardener module. --- ## 💡 Best Practices * **Context is King:** Provide at least 5 lines of context around Git diffs for more accurate neural reasoning. * **Continuous Gating:** Run the FinOps auditor before every infrastructure change, not after. * **Manual Sign-off:** Use AI findings as a high-fidelity signal, but maintain human-in-the-loop for kernel-level merges. --- ## 🔒 Security & Safety Notes * **Key Management:** Use CI/CD secrets for `GEMINI_API_KEY` in production. * **Least Privilege:** Test "Hardened" manifests in staging first to ensure no functional regressions. ## Links + - **Repository**: https://github.com/Champbreed/AegisOps-AI + - **Documentation**: https://github.com/Champbreed/AegisOps-AI#readme