🛡️ AegisOps-AI

Autonomous DevSecOps & FinOps Guardrails powered by Gemini 3 Flash

AegisOps-AI is a professional-grade "Living Pipeline" designed to integrate advanced AI reasoning directly into the software development lifecycle. By leveraging Gemini 3 Flash, it acts as an automated, intelligent gatekeeper for Linux Kernel security, Cloud Infrastructure costs, and Kubernetes compliance.

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🚀 Key Value Propositions

• The Living Pipeline: Unlike traditional scanners, AegisOps-AI is integrated into GitHub Actions (devsecops-audit.yml). It functions as an active quality gate that can block unsafe or non-compliant merges automatically.
• Full-Stack Context: AegisOps-AI bridges the gap between low-level systems (C-based Kernel patches) and high-level business operations (FinOps), providing a unified security posture across different technical domains.
• Advanced Reasoning: Built to handle complex tasks that traditional static analysis tools miss, such as identifying logic-based Use-After-Free (UAF) vulnerabilities and detecting massive cost-drifts in infrastructure plans.

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🤖 Generative AI Integration

AegisOps-AI leverages the Google GenAI SDK to implement a "Reasoning Path" for security audits:

• Neural Patch Analysis: Uses Gemini 3 to perform semantic code reviews of Linux Kernel patches, moving beyond pattern matching to understand memory state logic.
• Intelligent Cost Synthesis: Processes raw Terraform plan diffs through a financial reasoning model to detect high-risk resource escalations.
• Natural Language Policy Mapping: Translates human security intent into syntactically correct Kubernetes securityContext configurations.

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🧭 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 like p3.8xlarge. 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 user enforcement, and Capability dropping). Key Output: hardened_deployment.yaml

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🛠️ Setup & Environment

1. Clone the Repository:

   git clone [https://github.com/Champbreed/AegisOps-AI.git](https://github.com/Champbreed/AegisOps-AI.git)
   cd AegisOps-AI

Environment Setup:

python3 -m venv venv
source venv/bin/activate
pip install google-genai python-dotenv

API Configuration: Create a .env file in the root directory:

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

⚖️ License Distributed under the MIT License. See LICENSE for more information.

Author: Simon Essien (@Champbreed)