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Kaspersky Releases Guidelines for Securing AI Agents with Cyber Immunity

First seen 8 Oct 2026, 05:30 UTC • •

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ThreatCluster •October 8, 2026 at 06:32 UTC
  • •Kaspersky's report focuses on securing autonomous AI agents in corporate settings.
  • •It emphasizes Cyber Immunity principles for designing secure AI systems.
  • •Practical recommendations are provided for IT leaders to manage AI agent risks.

Kaspersky has published guidelines aimed at mitigating risks associated with autonomous AI agents in corporate environments. The report, titled 'AI agent security through the lens of Cyber Immunity,' was presented at the AI Everything Global expo in Abu Dhabi. It addresses the increasing use of AI agents in various business processes, including IT security functions. The guidelines emphasize the importance of defining security assumptions during the design phase, minimizing the Trusted Computing Base, isolating components, and controlling interactions with a default-deny approach. Kaspersky's recommendations target CISOs, CIOs, and CTOs, providing practical strategies to manage AI agent risks effectively. The report draws on real-world incidents to highlight potential vulnerabilities and outlines strategies to build trust in AI systems.

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Timeline

2026-10-06
Kaspersky presents AI security guidelines
Kaspersky unveiled its guidelines on AI agent security at the AI Everything Global expo in Abu Dhabi.
Kaspersky
2026-10-08
Guidelines published
Kaspersky released a report detailing the risks and security measures for AI agents in corporate infrastructures.
Kaspersky

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Common questions

What are the main risks associated with AI agents?
The report identifies risks such as malicious exploitation, excessive privileges, and data deletion by AI agents.
Who should implement these guidelines?
CISOs, CIOs, and CTOs are the primary audience for these guidelines to enhance AI security.
What is the Cyber Immunity approach?
Cyber Immunity focuses on building secure-by-design systems by embedding trust and minimizing vulnerabilities in AI architectures.