ESSENTIAL GUIDANCE · BUYER GUIDE · 3 min read
Part of Expose insights →AI security strategy for UK technology and security teams
Prioritise AI-related exposure, governance and assurance decisions without losing sight of established security controls.
Prioritise AI-related exposure, governance and assurance decisions without losing sight of established security controls.
Need an acronym translated?Open the cyber glossary →Start with the uses and the information involved
An AI strategy should distinguish where AI supports staff, where it acts on a workflow and where it can access or change important information. Record the systems, accounts, suppliers and business owners involved. A public commitment to use AI does not establish an inventory or a meaningful security boundary. Begin with the actual operating use case and the decisions it can influence.
Keep foundational controls in the programme
Identity, least privilege, secure configuration, software maintenance, sensitive-data handling and useful telemetry remain relevant when AI is introduced. Identify what changes in those controls when a new integration or automated workflow is added. Do not create a separate AI risk register that loses the connection to ordinary service ownership, engineering changes and operational incident handling.
Connect external exposure to informed action
Review the services, interfaces and credentials associated with the authorised external boundary. Use discovery and relevant intelligence to identify questions that need attention. A public signal is not proof of compromise or permission for active testing. Where additional evidence is required, define the assessment question and obtain the appropriate authorisation rather than allowing an automation label to widen the scope.
Make assurance requirements specific
Ask what capability or control is being assessed, which targets and methods are permitted and what the output should establish. AI-enabled penetration testing describes a delivery route, not certification of an AI model or proof that every AI assurance service is included. Avoid a catch-all capability claim where the technical scope, availability and delivery route have not been confirmed.
Review outcomes rather than adoption claims
Track whether important exposure decisions reached the right owners, whether evidence supported the response and whether changes were checked. Keep accountable people in the process and update boundaries as use cases evolve. The useful strategic question is how AI changes access, consequence and operating decisions in the organisation, not whether an AI label alone makes a service safer or more complete.
This guide covers the essentials. Continue into our technical analysis for a firmer position, practical implications and recommended action.
Read “Vulnerability remediation needs an operating model—not another spreadsheet” →
