Regtransfers/aegisops-ai

@ /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: never 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—e.g. accidental upgrades from t3.micro to high-performance GPU instances.
• Key Output: infrastructureauditreport.json

@ 3. ☸️ K8s Policy Hardener (k8spolicygenerator.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

git clone https://github.com/Champbreed/AegisOps-AI.git
cd AegisOps-AI

@ 2. Setup

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:

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:

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

@ Limitations

• Use this skill only when the task clearly matches the scope described above.
• never treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.