Explore the intersection of AI and cybersecurity. Stay informed on AI-driven security trends, tools, and threats in the ever-evolving digital landscape.
Search across headline titles and summaries.
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.
The National Security Memorandum on AI sets out actions for the federal government to ensure the safe, secure and trustworthy development of AI
A new ISACA study has revealed that cybersecurity professionals are often overlooked in the development of AI policies
70% of leaders see cyber knowledge gap; AI attacks are harder to detect, 60% expect more victims
AI tools are being used to launch over half a million cyber-attacks daily on retailers, according to a new report
A threat actor claimed to get hold of an exposed GitLab configuration file containing Zendesk API access tokens
Businesses in Australia must update their privacy policies with clear and transparent information about their use of AI, said the regulator