Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

TL;DR: AI governance solutions help organizations inventory, secure, and monitor AI systems. Best for AI security and shadow AI: Mend AI; enterprise risk and compliance: Credo AI and IBM watsonx.governance; model monitoring: Fiddler AI. Effective AI governance implementation involves establishing a cross-functional committee, compiling an AI bill of materials (AI-BOM) to identify risks, and implementing policies based on frameworks like NIST AI RMF.

Arctic Wolf Internal Security Operations (SecOps) recently identified a GitHub page impersonating Arctic Wolf to target our customers and prospects. The SecOps team immediately escalated these findings to our Threat Research team, who uncovered a complex attack chain subsequently deploying information-stealing malware. Arctic Wolf has since removed this fake GitHub page.

Phishing used to be the easy alert. Bad grammar. A link to “Amazon.com.” A sender address off by one character. Analysts joked about it. You almost felt bad for the attacker. That era is over. AI killed it.

Think of a disaster recovery (DR) plan as a grade-school fire drill for your data. Nobody plans on their school catching fire, but the organizations that practice the escape route to the exit are the ones who make it out calmly when the alarm bell sounds. The same logic applies to your IT environment. The disruption will come, whether it’s a cyberattack, hardware failure, or natural disaster.

For two decades, “penetration testing” has meant the same thing: once a year, you hire a firm, a human tester spends a week or two on your systems, and you get a PDF. Most compliance frameworks were written around exactly that ritual, a slow, manual, point-in-time engagement. Software doesn’t ship once a year anymore. It ships many times a day.