Explore the intersection of AI and cybersecurity. Stay informed on AI-driven security trends, tools, and threats in the ever-evolving digital landscape.
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Background for this topic.
Artificial intelligence (AI) describes computer systems that perform tasks such as recognizing patterns, making predictions, understanding language, or generating content. In security reporting, the term commonly includes machine-learning models used for detection and analysis, as well as generative AI applications that produce text, code, images, or other outputs.
AI can help analyze security telemetry, prioritize vulnerabilities, and support investigations, but its outputs can be wrong or manipulated. Important attack surfaces include prompt injection that steers an application into unintended actions, sensitive data being exposed through prompts or model outputs, and excessive permissions granted to AI systems that use external tools. Models can also be degraded by poisoned training data or evaded with carefully crafted inputs. Practitioners should protect training and operational data, limit model access and tool permissions, test for adversarial behavior, and require appropriate human validation before high-impact decisions.
Claude Security enters public beta, giving enterprises AI driven code scanning with no API integration or custom agents required
A researcher from offensive security firm Theori has found a nine-year-old flaw in the Linux kernel with the help of AI
Marsh’s 2026 People Risks survey finds cyber‑related challenges dominate, as cyber‑threat literacy tops risks and cyber and AI skills shortages rise
Researchers uncover a malicious npm dependency linked to an AI‑assisted code commit that steals sensitive data and exposes crypto wallets
Arctic Wolf attributed this large-scale spear-phishing campaign to BlueNoroff, a financially motivated subgroup of the Lazarus Group