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.
Microsoft will roll out the Copilot AI-powered assistant to Windows 10 systems enrolled in the Insider Program over the coming months. [...]
Fortinet is alerting customers of a critical OS command injection vulnerability in FortiSIEM report server that could be exploited by remote, unauthenticated attackers to execute commands through specially crafted API requests. [...]
The Criminal IP threat intelligence search engine by AI SPERA has recently integrated with Cisco SecureX/XDR, empowering organizations to stay ahead of malicious actors. Learn more about this integration from Criminal IP in this article. [...]