Markdown
Secure AI-Ops Middleware is a specialized forensic and operational framework designed to enable the safe use of Large Language Models (LLMs) within critical infrastructure environments.
In high-security sectors (Banking, Government, IP Protection), direct data transmission to external AI services poses a severe risk of data exfiltration. This framework establishes a strict Sanitization & Pre-processing Perimeter, ensuring that sensitive telemetry is anonymized locally before any external analysis occurs.
Mission: To leverage AI-driven mean-time-to-resolution (MTTR) reduction while maintaining a Zero-Trust data privacy posture.
The system implements a "Sanitization-First" architecture. No data leaves the secure zone without passing through the Redaction Engine.
+---------------------+ +---------------------------+ +-------------------------+
| SECURE PERIMETER | | MIDDLEWARE LAYER | | EXTERNAL / AI ZONE |
| | | | | |
| [Raw Logs] | | [ 1. Ingest Engine ] | | |
| /var/log/auth.log +-------> - Stream Processing | | |
| /opt/opentext/logs | | | | |
| | | [ 2. Sanitizer Core ] | | |
| | | - PII Detection (Regex) | | |
| [System Configs] +-------> - IP/Host Redaction +-------> [ Anonymized Vector ] |
| /etc/hosts | | - Tokenization | | (Safe for Analysis) |
| | | | | | |
| | | [ 3. Context Enricher ] | | v |
| | | - Metadata Tagging | | [ LLM / GPT-4 ] |
| | | | | | |
+---------------------+ +---------------------------+ +------------+------------+
|
<--- Insight / RCA Recommendation ---------+
🛡️ Core Modules
1. Data Privacy Engine (log_sanitizer.py)
Acts as the primary firewall for information leakage.
Zero PII: Automatically detects and masks Email addresses, User IDs, and Phone numbers.
Infrastructure Obfuscation: Replaces IPv4/IPv6 addresses and internal hostnames with generic tokens (e.g., [ASSET_01], [IP_REDACTED]).
Custom Dictionaries: Supports project-specific "Stop Words" to protect trade secrets (e.g., Project Codenames).
2. Infrastructure Forensics (JumpHost_Session_Forensics.py)
Provides behavioral analytics for jump servers and critical nodes.
Session Correlation: Maps SSH logins to disk I/O usage.
Anomaly Detection: Flags user sessions that exceed standard duration baselines.
Dormant Account Sweep: Identifies provisioned but inactive accounts that increase the attack surface.
3. OpenText Operational Intelligence (OT_License_Census.py)
specialized parsers for OpenText Content Server environments.
License Optimization: Differentiates between Named and Concurrent usage peaks.
Audit Summarization: Aggregates massive audit trails into actionable user behavior metrics.
🚀 Quick Start
Prerequisites
Python 3.8+
Standard libraries only (No external heavy dependencies required for core security modules).
Installation
Bash
git clone [https://github.com/your-repo/secure-ai-ops.git](https://github.com/your-repo/secure-ai-ops.git)
cd secure-ai-ops
Usage Example: Sanitizing a Crash Dump
To prepare a raw application log for AI-assisted root cause analysis:
Bash
# Run the sanitizer on a target log file
python log_sanitizer.py --input /var/log/app/crash.log --output ./clean_context.txt
# Output:
# [SUCCESS] Processed 15,000 lines.
# [SECURE] Redacted 42 IP addresses and 12 User IDs.
# [READY] Output saved to ./clean_context.txt
🔒 Compliance & Standards
This toolkit is engineered to support alignment with the following frameworks:
GDPR (Art. 5): Adheres to data minimization principles.
ISO/IEC 27001: Supports controls for information masking and operational logging.
NIST SP 800-53: Assists in Audit and Accountability (AU) controls.
🤝 Roadmap
[ ] Vector DB Integration: Local embedding storage for RAG (Retrieval-Augmented Generation).
[ ] Real-time Stream: Piping tail -f directly through the sanitizer.
[ ] EVTX Parser: Native Windows Event Log support.