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.