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.
Chinese national Linwei Ding is accused of pilfering more than 500 files containing Google IP while affiliating with two China-based startups at the same time.
Election cyber threats come from various places, including compromised voting machines, AI deepfakes, and potential physical harm to workers. But CISA has been working diligently with various public and private partners to stymie the risk.
Handing over your business data to artificial intelligence companies comes with inherent risks.
Security for AI is the Next Big Thing! Too bad no one knows what any of that really means.
The wide availability of generative AI will make synthetic identity fraud even easier. Organizations need a multilayered defense to protect themselves.