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.
AI tools are not just creating new vulnerabilities, they are reviving old security failures, warned Jurgen Kutscher, VP of Mandiant Consulting
Google Cloud’s COO advocated for combining general-purpose frontier large language models with task-specific AI agents
Google Cloud will attribute a unique cryptographic ID every AI agent that will be tied to “traceable and auditable” authorization policies
Forcepoint has found 10 new indirect prompt injection attacks targeting AI agents
Data exposure, operational disruption and financial losses among issues faced by businesses struggling with the rapid rise of AI agents, warns Cloud Security Alliance report