Aegisops ai

--- 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 GEMINIAPIKEY` 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