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.
Cobalt study finds 20-percentage-point drop in number of organizations relying solely on AI automation for testing
LayerX tricked AI browsers including ChatGPT Atlas and Comet into bypassing their guardrails
SentinelLabs found a North Korea-linked macOS backdoor using prompt injection on AI triage tools
New ReliaQuest study reveals the six ways AI is practically being used in attacks today
OpenAI expanded Daybreak with a full GPT-5.5-Cyber release to help defenders patch software flaws
The Five Eyes Alliance has published a rare call to action for organizations facing AI threats
North Korean threat actor Sapphire Sleet has been linked to a supply chain attack targeting Mastra, according to Microsoft security researchers