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: 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

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 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

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags aegisops-ai <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns