{"id":15266,"date":"2025-12-04T20:32:39","date_gmt":"2025-12-04T20:32:39","guid":{"rendered":"https:\/\/newestek.com\/?p=15266"},"modified":"2025-12-04T20:32:39","modified_gmt":"2025-12-04T20:32:39","slug":"ai-creates-new-security-risks-for-ot-networks-warns-nsa","status":"publish","type":"post","link":"https:\/\/newestek.com\/?p=15266","title":{"rendered":"AI creates new security risks for OT networks, warns NSA"},"content":{"rendered":"
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The security of operational technology (OT) in critical infrastructure has been a recurring theme for years, but this week the US National Security Agency (NSA) and its global partners added a new concern to the mix: how the increasing use of AI in OT risks making things worse.<\/p>\n

The scope of these concerns, and guidance for addressing them, is outlined in the Principles for the Secure Integration of Artificial Intelligence in Operational Technology<\/em><\/a>,<\/em> authored by the NSA in conjunction with the Australian Signals Directorate\u2019s Australian Cyber Security Centre (ASD\u2019s ACSC) and a global alliance of national security agencies.<\/p>\n

While the use of AI in critical infrastructure OT is in its early days, the guidance reads like an attempt by the NSA and its partners to get ahead of the problem before misuse or misapplication becomes entrenched. Although drafted for OT admins, the guidelines mirror concerns that also apply to IT administration.<\/p>\n

Currently, AI is being put to work in OT networks in the energy, water treatment, healthcare, and manufacturing sectors for the same reason it is being used elsewhere: to optimize and automate processes, thereby improving efficiency and uptime.<\/p>\n

The worry is that organizations are jumping into a new and far from battle-hardened technology without assessing its limitations, echoing what has been happening in IT. Measuring risk<\/a> against the industrial control systems (ICS) Purdue Model hierarchy<\/a>, the guidelines enumerate worries such as adversarial prompt injection and data poisoning, data collection leading to reduced safety, and \u201cAI drift\u201d in which models become less accurate as new data diverges from training data.<\/p>\n

Also mentioned: AI can lack the explainability necessary to diagnose errors, there are difficulties meeting compliance requirements as AI rapidly evolves, and there\u2019s a human de-skilling effect caused by a creeping over-dependence on AI. Likewise, AI alerts might lead to distraction and cognitive overload among employees.<\/p>\n

Finally, the tendency of AI technologies such as chatbots and LLMs to hallucinate raises doubts about whether the technology is robust enough to be used in environments where safety is a priority. \u201cAI may not be reliable enough to independently make critical decisions in industrial environments. As such, AI such as LLMs almost certainly should not be used to make safety decisions for OT environments,\u201d said the authors.<\/p>\n

This underlines an important difference between using AI in an OT setting and an IT one \u2013 OT networks are by nature safety-critical. Although many of the issues are the same, the margin for error is much smaller.<\/p>\n

Struggling to unwind<\/h2>\n

\u201cThe guidance raises the right questions: what risks are we introducing, what value does AI truly bring, who is accountable for oversight, and how do we respond when the technology misbehaves?\u201d commented Sam\u00a0Maesschalck<\/a>,\u00a0an OT engineer\u00a0with cyber security training platform Immersive\u00a0Labs. \u201cWe\u2019ve\u00a0already seen what happens when operational demands outpace secure design. IT\/OT convergence brought efficiency, but it also exposed OT networks in ways the industry is still struggling to unwind.\u201d<\/p>\n

According to Maesschalck, grafting AI systems onto OT infrastructure would fail if pre-existing issues aren\u2019t addressed first. These include the inability of some OT devices to feed the required volumes of data to AI platforms, and a lack of asset inventories that make problem interactions more difficult to predict.<\/p>\n

Among the guidelines\u2019 recommendations are for organizations to adopt CISA\u2019s secure design principles<\/a>, and to assess whether developing an AI-OT project inhouse would give organizations more control over AI design and implementation in the long run.<\/p>\n

\u201cThis kind of guidance is influential because operators are looking for clarity. Having government-backed principles to reference gives owners and engineers something concrete to point to when they push back on unsafe or rushed adoption. It also reinforces how essential education is,\u201d said Maesschalck.<\/p>\n

The guidelines arrive on the heels of last year\u2019s NSA and ACSC report<\/a> listing the steps organizations should take to secure OT in critical infrastructure. But neither document addresses continuing concerns that OT security still doesn\u2019t get the budget it warrants<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The security of operational technology (OT) in critical infrastructure has been a recurring theme for years, but this week the US National Security Agency (NSA) and its global partners added a new concern to the mix: how the increasing use of AI in OT risks making things worse. The scope of these concerns, and guidance for addressing them, is outlined in the Principles for the… <\/p>\n

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