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<\/a><\/p>\n Justin Doubleday<\/a>@jdoubledayWFED<\/a><\/p>\n \n June 18, 2026 10:27 am <\/p>\n 3 min read<\/p>\n With many agencies looking to adopt artificial intelligence, including agentic AI, forward-leaning cybersecurity policies and practices may be more important than ever.<\/p>\n The federal government is moving to adopt AI<\/a> to streamline both internal processes and public interactions. But AI adoption needs to come with proper safeguards, said Michael Epley, chief architect and security strategist at Red Hat<\/a>.<\/p>\n \u201cWe are deploying these AI agents mostly because we want to gain the automation, the efficiency of these generative tools and apply those to real world problems,\u201d Epley said during Federal News Network\u2019s Cloud Exchange 2026<\/a>.<\/p>\n \u201cThe real challenge is we\u2019re deploying these AI agents without strong frameworks for understanding how to control these agents. We\u2019re granting them very broad access because we want these agents to essentially automate what humans do, but we\u2019re not constraining those AI agents.\u201d<\/p>\n Organizations may be granting AI agents broad access to data and tools to streamline their operations, but that also requires corresponding controls, he said.<\/p>\n \u201cSome of these existing frameworks are great in that we can leverage what we already know about how to apply these frameworks, typically designed around how human actors work in our systems, and apply those to AI agents using the same principles,\u201d Epley said.<\/p>\n Zero trust and traceability<\/p>\n He pointed the zero trust cybersecurity architecture that agencies have been directed to adopt since 2021. Zero trust presumes a network or system will be breached and adopting controls that limit the damage.<\/p>\n \u201cApplying those means things like least privilege,\u201d he said. \u201cEven though we want our autonomous agents to act independently, it also means only granting them access to the tools and the systems and the data that they actually need to operate in a given time, and then revoking those accesses when they\u2019re no longer needed, and doing that on a per agent or per access basis.\u201d<\/p>\n Epley also argued actions taken by an AI agent should ultimately be \u201ctraceable\u201d to a human being who takes ownership of the actions and outcomes.<\/p>\n \u201cThat traceability can be done through, say, token exchanges or other techniques that mean that the agent is really acting on behalf of a human,\u201d he said. \u201cEven though it\u2019s operating autonomously through that agent process, but ultimately that\u2019s traceable back to a human that\u2019s in control and responsible for that interaction.\u201d<\/p>\n AI security concerns<\/p>\n Recent advancements in the cyber capabilities of models like Anthropic\u2019s Claude Mythos have further entrenched security concerns. Advanced models have demonstrated the ability to quickly find software vulnerabilities and exploit them.<\/p>\n \u201cAI is unlike humans. It\u2019s very persistent,\u201d Epley said. \u201cIt doesn\u2019t give up, it doesn\u2019t get bored, and it will continue. If a human directs it to find a vulnerability or find a weakness in your cloud or your infrastructure, it will find those.\u201d<\/p>\n While that could be a boon for nefarious hackers, the cybersecurity community is also beginning to use those models to find and patch vulnerabilities in areas like open source software.<\/p>\n \u201cThis has really highlighted the power of AI but also the need to have things like strong controls and zero trust to constrain it \u2014 and to make sure that these AI systems do not exceed their boundaries and the controls that we apply to those AIs to limit those actions,\u201d Epley said.<\/p>\n Discover more articles and videos now on the Cloud Exchange event page<\/a>.<\/p>\n Copyright
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