Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The 89% Problem: How LLMs Are Resurrecting the "Dormant Majority" of Open Source

AI coding assistants are quietly resurrecting millions of abandoned open source packages. For the last decade, developers relied on a simple heuristic for open source security: Prevalence \= Trust. If a package was downloaded millions of times a week (lodash, react, requests), we assumed it was "safe enough" because thousands of eyes were on it. If it was obscure, we approached with caution.

AI Compliance: 5 Key Frameworks, Challenges, and Best Practices

AI compliance ensures AI systems follow laws, ethics, and standards by managing risks like bias, privacy violations, and lack of transparency through robust governance, documentation, and continuous monitoring, using frameworks like the EU AI Act and NIST AI Risk Management Framework (RMF) to build trust and avoid penalties in developing, deploying, and operating AI.

AI Moves Fast, Privacy Has to Move Faster with Ojas Rege

In this episode, Caleb Tolin welcomes Ojas Rege of OneTrust for a practical, wide-ranging conversation on how data privacy and governance must evolve alongside enterprise AI adoption. Ojas explains why AI fundamentally changes the privacy conversation: the same systems that enable organizations to move faster can also cause harm faster when guardrails aren’t in place. From agentic AI systems that dynamically repurpose data to general-purpose models that blur traditional notions of “intended use,” the challenge isn’t just compliance—it’s trust.

AI Agent Sandboxing & Progressive Enforcement: The Complete Guide

Your CISO just got word that engineering is deploying AI agents into production Kubernetes clusters next quarter. Not chatbots—autonomous agents that generate and execute code, call external APIs through MCP tool runtimes, access internal databases, and make decisions without human review. The question lands on your security team: “How are we securing these?”

AI-Aware Threat Detection for Cloud Workloads: 4 Attack Chains Most Security Stacks Miss

Your security stack was built for workloads that follow predictable code paths. AI agents don’t. They interpret prompts, generate code on the fly, invoke tools dynamically, and escalate privileges in ways no developer anticipated — all as part of normal operation. The signals that indicate a compromise in a traditional container are indistinguishable from an AI agent doing its job. And most detection tools can’t tell the difference. This isn’t a theoretical gap.

AI Security Posture Management (AI-SPM): The Complete Guide to Securing AI Workloads

Every cloud security vendor now has an AI-SPM dashboard. Strip away the branding, though, and most of these dashboards are doing the same thing: checking IAM configurations, scanning for misconfigured network access, inventorying AI models across cloud accounts, and flagging compliance gaps. It’s cloud security posture management with an AI label applied. That’s a problem, because AI workloads don’t behave like other cloud workloads.