Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

mend

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

Mar 3, 2026 By Tiffany Jennings In Mend
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.
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AI Moves Fast, Privacy Has to Move Faster with Ojas Rege

Mar 3, 2026 By Rubrik In Rubrik
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.
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armo

AI Agent Sandboxing & Progressive Enforcement: The Complete Guide

Mar 3, 2026 By Ben Hirschberg In ARMO
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?”
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armo

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

Mar 3, 2026 By Yossi Ben Naim In ARMO
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.
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armo

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

Mar 3, 2026 By Shauli Rozen In ARMO
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.
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nightfall

AI Can Scan Your Code. It Can't Secure Your Organization.

Mar 3, 2026 By Chris Martinez In Nightfall
When Anthropic announced Claude Code Security on February 20th—a tool that scans codebases for vulnerabilities and suggests patches for human review—the reaction from markets was swift and brutal. Major cybersecurity names watched their stock prices fall by double digits within days. The implied thesis behind the selling: AI can now do what these companies do, so why pay for them? It's a compelling fear and an inaccurate conclusion at the same time. The DLP space is a clear example of why.
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aikido

Rare Not Random: Using Token Efficiency for Secrets Scanning

Mar 3, 2026 By Zach Rice In Aikido
In Regex is (almost) All You Need, we learned that using a combination of regular expression patterns, entropy, and rule-based filters are an effective way to detect candidate secrets. Regex is used for casting a wide net to identify candidates. Entropy is used as a primary filter on the captured candidates and additional filters like presence of commonly used english words, or filtering on known “safe” files like go.sum are applied last.
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