| name | aegisops-ai |
| description | Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance. |
| type | skill |
| created | "2026-02-27T00:00:00.000Z" |
| domain | ai-ml |
| category | llm-agents |
| risk | safe |
| source | community |
| tags | ["skill","ai-ml","llm-agents","aegisops"] |
/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:
- Identifying logic-based vulnerabilities (UAF, Stale
State) in Linux Kernel patches.
- Detecting massive "Silent Disaster" cost drifts in
Terraform plans.
- 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
Connections
- Domain: [[KI & Machine Learning]]
- Kategorie: [[LLM & KI-Agenten]]
- Navigation: [[Skills Uebersicht]], [[Home]]
