AI security
AI security establishes the controls that keep artificial intelligence systems trustworthy, reliable, and aligned with business goals. It reduces the risk of data leaks, model abuse, and compliance failures and helps organizations adopt AI with confidence.
AI TRiSM: trust, risk, and security management explained
Artificial Intelligence Trust, Risk, and Security Management (AI TRiSM) is a framework introduced by Gartner. Its aim is to k...
What is LLM security?
In June 2025, Microsoft published a security advisory about a flaw in Microsoft 365 Copilot. The vulnerability, tracked as CV...
AI data poisoning: attacks, examples, and how to prevent it
AI data poisoning involves manipulating and corrupting the data used to train artificial intelligence (AI) or machine learnin...
AI security best practices: a guide for enterprises
Artificial intelligence is now part of daily work in many enterprises. Copilots draft emails, agents trigger workflows, and s...
AI risk management framework explained
AI risk management is the practice of finding, assessing, reducing, monitoring, and reporting the risks that come from AI sys...
AI cybersecurity threats
AI is no longer some future risk—it is speeding up cyberattacks on an unprecedented scale. The widespread adoption of artific...
How to create a strong AI security policy
Employees are now adopting AI tools at such a pace that many security teams can’t keep up. As a result, companies are losing ...
AI ransomware: what it is and how to fight back
AI ransomware is malware that uses artificial intelligence to automate target selection, evade security tools, and adapt its ...
AI social engineering: techniques, risks, and how to prevent it
AI social engineering is the use of artificial intelligence, such as generative text, voice cloning, and deepfake technology,...