Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

7 AI Governance Tools for Shadow AI Detection

AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI — often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.

Claude DLP: Secure Your Sensitive Data in Agentic AI

Anthropic Claude has become a powerhouse for enterprise organizations, supercharging everything from software engineering to data analysis. But with great agentic AI power comes an equally massive security challenge. Every prompt, snippet of code, or piece of customer PII fed into the Claude platform represents a potential data leak or compliance breach. To safely leverage AI’s full potential, CISOs and engineering leaders can no longer rely on yesterday’s security stack.

7 Hidden Risks of AI in the Workplace

Is your team using AI tools at work? Without the right guardrails, you could be exposing your business to data breaches, compliance violations, and serious reputational damage — and most companies don't see it coming. In this video, we break down the 7 hidden risks of AI in the workplace — from data privacy breaches and AI hallucinations to Shadow AI, prompt injection attacks, and intellectual property complications. We also cover the best practices every organization needs to manage workplace AI risks before they become costly problems.