Explore practical AI security insights, emerging threats, governance frameworks, secure AI development, and risk-management guidance. Learn how organizations can protect AI models, data, applications, and users from evolving cybersecurity risks.
To understand how to build an AI-assisted vulnerability scanner as a beginner, you don’t need a vendor budget; what you need is the underlying workflow: trace untrusted data from a source to a sink (taint analysis), use an LLM to slice code and reason about that data flow, then manually verify every finding. Practice on a permitted target, log your results, and you’ve got a real, portfolio-worthy skill
Four separate 2026 reports show AI coding agents like Claude Code, Cursor, and Copilot can be tricked into running malicious code, bypassing approval prompts, or installing hallucinated malware packages. Here’s a practical checklist to lock down permissions before you get burned.
The NIST IR 8596 Secure Profile explains how organizations can secure AI systems by applying cybersecurity controls across governance, asset visibility, protection, monitoring, incident response, and recovery. It translates AI-specific risks such as prompt injection, data poisoning, adversarial inputs, model compromise, and AI supply chain exposure into a structured control roadmap aligned with the NIST Cybersecurity Framework 2.0.
Introduction Claude Chrome Extension incident highlighted that how zero-click GenAI vulnerabilities can turn trusted AI systems into attack pathways The
While artificial intelligence (AI) has transformative possibilities, it also poses novel cybersecurity threats that conventional security measures might not be able to adequately counter. The National Institute of Standards and Technology (NIST) has acknowledged this by publishing a concept paper on Control Overlays for Securing AI Systems (COSAIS).
False positives in SOC, like their counterpart, false negatives, are among the most critical issues in modern cyber protection. Security Information and Event Management (SIEM) systems and Security Operations Centers (SOC) form the backbone of monitoring, but detection is never perfect
Zero-Day AI attacks don’t target just code — they exploit models, prompts, and data. From ShadowLeak to prompt injection, here’s why AI threats are different and urgent in 2025
AI vs AI in cybersecurity is becoming the ultimate battlefield. Imagine checking your email and seeing a message from the boss urging you to send money quickly. The email looks excellent, the tone reflects the way they speak, and the signature line is perfect. But there’s one problem: your boss never sent it.
