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Latest coverage for Artificial Intelligence

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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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.

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Artificial Intelligence (AI) is changing how individuals and organizations conduct many activities, including how cybercriminals carry out phishing attacks and iterate on malware. Now, cybercriminals are using AI to generate personalized phishing emails, deepfakes and malware that evade traditional detection by impersonating normal user activity and bypassing legacy security models. As a result,

When a Magecart payload hides inside the EXIF data of a dynamically loaded third-party favicon, no repository scanner will catch it – because the malicious code never actually touches your repo. As teams adopt Claude Code Security for static analysis, this is the exact technical boundary where AI code scanning stops and client-side runtime execution begins