/aegisops-ai — Autonomous Governance Orchestrator

AegisOps-AI is described by its upstream as a governance 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.

1. Detecting massive "Silent Disaster" cost drifts in

Terraform plans.

1. 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: A configured model proposes findings on raw Git diffs; verify each reachable trigger and consequence through code review and appropriate tests. No latency or detection guarantee is made.
• 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 candidate YAML manifests requiring schema, policy and application validation (Read-only root FS, Non-root enforcement, etc.).
• Key Output: hardened_deployment.yaml

🛠️ Setup & Environment

AAS bundles this guide only: patch_analyzer.py, cost_auditor.py, k8s_policy_generator.py, main.py and their reports are upstream components, not local bundled files. Verify the upstream repository, a fixed revision, license, dependencies, model availability and actual entry point before using the tool. Treat provider findings as advisory, not an automatic CI merge authority.

Obtain authorization for paid model calls and transmission of private patches, plans or cluster configuration. Redact secrets and personal/account identifiers; do not assume .env storage is automatically secure.

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

Use the host's existing private credential mechanism for GEMINI_API_KEY. If a local .env is required by the reviewed upstream implementation, ensure it is owner-only and gitignored, preserve any existing file and never display its contents. Confirm the installed SDK/model combination instead of assuming an old model alias remains available.

🏁 Operational Dashboard

Only after reviewing the fixed upstream checkout and authorizing its network/cost effects, its documented entry point may be run there:

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

• **C