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.
The Coalition for Secure AI is a consortium of influential AI companies aiming to develop tools to secure AI applications and set up an ecosystem for sharing best practices.
Digital literacy and protective measures will be key to detecting disinformation and deepfakes as AI is used to shape public opinion and erode trust in the democratic processes, as well as identify nefarious content.
With AI use ramping up rapidly, a growing number of enterprise security teams have begun putting controls in place to protect sensitive data from accidental exposure and leaks.
An AI consortium consisting of top tech companies will release a toolkit later this year for measuring the safety of generative AI models.