Topic: ai risk management
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Gurucul AI-IRM: Detect Insider Threats Faster
Gurucul has launched the AI Insider Risk Management (AI-IRM) platform to enhance detection and response to insider threats through autonomous triage and bias-free risk scoring, addressing a sharp rise in such incidents. The platform integrates User and Entity Behavior Analytics (UEBA), identity a...
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AI's Self-Poisoning Threatens Model Collapse - Here's the Fix
AI systems face a critical threat called "model collapse", where training on unverified, AI-generated data leads to unreliable and biased outputs, undermining automated decision-making. To combat this, organizations are predicted to adopt a "zero-trust approach to data governance", requiring ...
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How to Build Trustworthy and Secure AI for Cyber Resilience
Securing AI systems is now as critical as using AI for defense, requiring a shift to cyber resilience that ensures these systems can withstand and recover from sophisticated attacks. The evolving threat landscape includes AI-specific risks like data poisoning, model theft, and prompt injection, n...
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AI Leadership in the Algorithm Age: What's Changed, What Endures
Modern executives must integrate technological fluency with human-centered leadership, balancing AI's power with ethical judgment and human connection to steer organizations responsibly. Experts emphasize that leaders need technical literacy to distinguish between algorithmic and human decisions,...
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Who Polices Police AI? Perplexity's Deal Alarms Experts
A new AI program is being offered to police departments to analyze evidence and generate reports, raising significant concerns about accountability and safety in law enforcement. Experts warn that even routine administrative uses of AI, like summarizing transcripts, pose serious risks of errors t...
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Zscaler Boosts AI Security with Enhanced Visibility and Control
Businesses are rapidly adopting AI, but this creates new security vulnerabilities as traditional cybersecurity tools fail to protect AI systems' unique traffic and protocols. A major challenge is "shadow AI," where companies lack a complete inventory of their AI assets, creating blind spots that ...
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