Automated Integrity Checks & Anomaly Detection

My Approach

I’ve worked in environments where “trust but verify” wasn’t enough — you had to verify constantly. That taught me that integrity checking has to be built into the system, not left as a manual afterthought.

• File & Config Hashing: Generate and compare hashes on configs and binaries to catch tampering or drift.

• Baseline State Monitoring: Define “known good” configs and automatically flag deviations.

• Automated Triggers: Kick off alerts or rollbacks if integrity checks fail, instead of letting issues sit unnoticed.

• Anomaly Detection: Correlate logs and performance counters to spot patterns that don’t fit normal baselines.

Advancing Further

• Scheduled Integrity Scans: Run checks regularly, not just during incidents.

• SIEM Integration: Push results into SIEM platforms for centralized correlation.

• ML-Augmented Detection: Use anomaly detection models to spot subtle changes humans might miss.

Why It Matters

I’ve seen how a single unnoticed config change or tampered file can spiral into a breach or outage. Automated integrity checks close that gap — they don’t just make you more secure, they give you confidence that what’s running is what you think is running. In my experience, that confidence is priceless in secure and regulated environments.