{"id":68184,"date":"2026-09-14T17:38:00","date_gmt":"2026-09-14T17:38:00","guid":{"rendered":"https:\/\/www.elixirr.com\/en-gb\/?page_id=68184"},"modified":"2026-09-15T16:10:36","modified_gmt":"2026-09-15T16:10:36","slug":"cybersecurity-ai-security-controls","status":"publish","type":"page","link":"https:\/\/www.elixirr.com\/en-gb\/services\/data-technology\/cybersecurity\/cybersecurity-ai-security-controls\/","title":{"rendered":"AI Security Controls"},"content":{"rendered":"\n
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\n Cybersecurity<\/p>

\n AI Security Controls<\/h1>

\n A practical, enterprise-wide control baseline for safe AI adoption that is aligned to how your teams actually deliver, so usage can accelerate without accelerating risk. <\/h6>
\n Contact us<\/span><\/a> <\/div>\n <\/div>\n
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\n \n Cybersecurity Strategy <\/a>\n \n Cyber Resilience <\/a>\n \n Cyber Risk Management <\/a>\n \n Cyber Risk Assessment <\/a>\n \n Human Factor Cyber Risk Transformation <\/a>\n \n Agentic AI Security <\/a>\n \n Agentic Harnesses for Cybersecurity <\/a>\n \n Competitive Tabletop Exercises <\/a>\n <\/div>\n <\/div>\n <\/div>\n \n <\/div>\n\n \n \n
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\n Control AI adoption before it outpaces you<\/h2>
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AI is moving from experimentation into enterprise delivery, and its risks are becoming more operational and more connected. We help clients define and implement a clear control environment to govern, secure and scale AI use cases with confidence, covering governance, assurance and lifecycle expectations. The outcome is:<\/p>\n

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  • An end-to-end view of your current AI control posture and how it aligns to your operational reality, ambition and security expectations<\/li>\n
  • A practical control baseline with clear ownership, accountability and decision rights<\/li>\n
  • A prioritised roadmap for moving from experimentation to scaled production<\/li>\n
  • Guardrails for data exposure, prompt injection, retrieval security and human oversight, that leadership can stand behind<\/li>\n<\/ul>\n <\/div>\n <\/div>\n <\/div>
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    \n A structured route to a working control baseline<\/h2>
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    More AI use cases now touch sensitive data, internal systems and live workflows, but adoption is outpacing control environments that are often fragmented or borrowed from traditional application models. AI raises new questions on data exposure, prompt injection, retrieval security, access, model change and human oversight. Without a practical baseline, organisations struggle to define what good looks like and where to prioritise remediation, and end up either over-restricted and losing value, or under-controlled and accumulating risk.<\/p>\n <\/div>\n <\/div>\n <\/div>

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    \n From experimentation to enterprise-ready<\/h2>
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    We map where AI is used across your organisation, assess the current control environment against your risk profile, and translate the findings into a prioritised roadmap for closing the gaps that matter most.<\/p>\n <\/div>\n

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    Typical applications include building an enterprise AI security control baseline, defining governance and ownership for AI adoption, and aligning controls across business, security, data and engineering teams. It also supports organisations preparing for regulatory and board scrutiny of AI deployments, and prioritising control uplift as they move from pilot to scaled production.<\/p>\n <\/div>\n <\/div>\n <\/div> <\/div>\n<\/section>\n\n\n\n

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    \n Scale AI without scaling risk<\/h2>
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    As AI moves from experimentation to enterprise delivery, the right controls can help you accelerate adoption while protecting sensitive data, systems and workflows.<\/p>\n

    Don\u2019t let AI adoption outpace your control environment, speak to our consultants today.<\/p>\n <\/div>\n <\/div>\n <\/div> <\/div>\n \n \n \n <\/div>\n\n

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