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UK ICO publishes AI and biometrics strategy: what organisations need to know

The ICO’s June 2025 strategy and March 2026 update target AI decision-making, foundation models, recruitment, public services and police facial recognition.
From TheFinanceBase Team7 min to read
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The UK Information Commissioner’s Office (ICO) launched its AI and biometrics strategy on 5 June 2025. Called Preventing harm, promoting trust: our AI and biometrics strategy, it sets out where the regulator will focus its work—not a new, all-purpose UK AI law or universal approval system.

The ICO’s March 2026 update shows that its priorities now include automated decision-making, foundation models, AI-assisted recruitment and public services, and police facial recognition. Organisations using these technologies must continue to comply with existing UK data-protection law while preparing for further guidance, statutory codes and possible changes to the law.

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What the ICO strategy is—and is not

The strategy is a forward-looking regulatory roadmap. It explains how the ICO intends to help organisations use AI and biometric technologies lawfully and responsibly, while investigating and enforcing where personal information is misused or creates serious risks.

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It applies across public- and private-sector activity, although the ICO is concentrating on uses capable of significantly affecting people’s jobs, access to services, privacy, safety or rights.

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It is not equivalent to the EU AI Act. The strategy does not itself create a single licensing regime for every AI system, and it does not replace the UK GDPR or the Data Protection Act 2018. Its practical effect comes through the ICO’s existing regulatory powers, published guidance, audits and enforcement, together with planned statutory codes and possible future legislation.

The ICO announced the strategy on 5 June 2025. The March 2026 update is the more useful reference point for its implementation.

Why AI and biometrics are a priority

AI can improve efficiency, security and public services, but its errors can be difficult to detect and hard for an individual to challenge. A model may rely on inaccurate or unrepresentative data, reproduce discrimination, infer sensitive characteristics or make decisions that are effectively impossible to explain.

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Biometric systems create additional concerns because physical or behavioural characteristics can be used to identify, verify or classify people. Unlike a password, a face or fingerprint cannot normally be replaced after compromise.

The ICO’s strategy page says that, in 2024, 8% of UK organisations reported using AI decision-making tools when processing personal information and 7% reported using facial or biometric recognition. These are ICO-reported survey figures, not a complete census of UK adoption.

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The ICO’s four main workstreams

1. AI, profiling and automated decision-making

The ICO is developing clearer expectations around:

  • transparency and explainability;
  • fairness, bias and discrimination;
  • accountability and governance;
  • meaningful human involvement;
  • individual rights and redress;
  • special-category data;
  • lawful model training; and
  • automated decisions and profiling.

A planned statutory AI and automated decision-making code is intended to provide practical guidance on developing and deploying AI consistently with data-protection law. The March 2026 update says that the government was still developing the secondary legislation needed to require the ICO to produce the code. It should therefore be treated as planned, not final law.

The ICO was also preparing automated-decision-making and profiling guidance for consultation. Until new material is finalised, organisations should use the ICO’s AI guidance hub alongside the UK GDPR and Data Protection Act 2018.

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2. Foundation models and generative AI

The ICO says it is engaging with 11 major foundation-model developers about data-protection compliance and measures to reduce harm. That figure reflects the ICO’s position in the March 2026 update and may change.

The regulator’s interest covers the model lifecycle, including:

  • how training data is collected and sourced;
  • lawful basis, web scraping and crawling;
  • special-category data in training sets;
  • training, fine-tuning and retention;
  • security and data-subject rights;
  • risks created by model outputs; and
  • governance of downstream deployments.

This is a data-protection focus. The strategy does not settle every question about copyright, competition, product safety or general AI safety. Developers and deployers must still analyse their own purposes, data, roles and risks.

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3. Recruitment and public services

The ICO is working with public and private organisations on automated decision-making in recruitment and plans to publish findings and recommendations alongside broader guidance.

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Examples include automated CV filtering, candidate ranking, suitability scoring, interview analysis, workplace monitoring, fraud detection, welfare eligibility decisions and prioritisation of public services.

The key legal and practical questions are:

  • Is the decision solely automated?
  • Does it have legal or similarly significant effects?
  • Can a reviewer genuinely change the result?
  • Are people told that automation is being used?
  • Is the input data accurate and representative?
  • Can affected people challenge the outcome?
  • Is special-category data used, or inferred indirectly?

A human sign-off is not automatically meaningful oversight. Reviewers need enough information, time, training and authority to investigate and overturn an output. If staff are expected to accept the system’s recommendation in practice, the process may remain effectively automated.

4. Facial recognition and police biometrics

The ICO plans to support proportionate and rights-respecting police use of facial-recognition technology through guidance, audits and advice to government. Its March 2026 update says that it had continued its police facial-recognition audit programme, published audit-related material and research into public views, and responded in February 2026 to a Home Office consultation on a possible new legal framework.

These technologies should not be treated as one legal category:

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  • Live facial recognition matches faces captured in real time against a watchlist.
  • Retrospective facial recognition searches images captured previously.
  • Facial verification checks whether a person is who they claim to be.
  • Biometric categorisation or inference derives classifications or traits from physical or behavioural characteristics.

The Home Office consultation, published on 4 December 2025, concerns a proposed framework for law-enforcement use of biometrics, facial recognition and similar technologies. The framework was not, on the evidence available here, settled or enacted. The ICO’s February 2026 response sets out its policy position, not a statement of completed law.

What law applies now?

Organisations already processing personal data through AI or biometrics must assess their obligations under existing law. Relevant requirements include:

  • a lawful basis for processing;
  • fairness and transparency;
  • purpose limitation and data minimisation;
  • accuracy;
  • storage limitation and security;
  • accountability and documentation;
  • data-subject rights;
  • data-protection impact assessments (DPIAs);
  • special-category-data conditions; and
  • the safeguards applying to solely automated decisions and profiling.

Biometric data used to uniquely identify someone will generally raise special-category-data issues and require particularly careful analysis of necessity, proportionality, lawful conditions and safeguards. However, not every photograph, voice recording or behavioural signal automatically becomes biometric data. Classification depends on how the information is processed and used.

A DPIA is a risk-management and accountability exercise, not an ICO permission slip. Completing one does not make an otherwise unlawful system lawful.

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What organisations should do now

  1. Build an inventory. Record every AI and biometric system, its supplier, purpose, data sources, users, locations and affected groups.
  2. Rate the impact. Give enhanced scrutiny to recruitment, policing, welfare, healthcare, education, financial decisions, access control and systematic monitoring.
  3. Complete a DPIA where required. Consider large-scale special-category processing, biometric identification, systematic monitoring and decisions with significant effects.
  4. Document the legal basis. Identify both the ordinary lawful basis and, where relevant, the separate condition for special-category processing.
  5. Test accuracy and unequal impact. Measure false positives, false negatives and subgroup performance in conditions resembling actual use.
  6. Make human review real. Give reviewers evidence, training and authority to question or reverse an output.
  7. Explain and provide redress. Tell people what the system does, what data it uses, how automation affects them and how to request review.
  8. Control suppliers. Address data provenance, retention, security, subprocessors, audit access, incident reporting, model changes and deletion.
  9. Monitor after launch. Check for model drift, changed data sources, altered vendor functionality and new harms.
  10. Keep evidence. Retain DPIAs, testing, approvals, notices, logs, complaints, review records and remediation decisions.
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Sector-specific risks

Employers

Review automated CV screening, personality or “culture fit” scores, emotion analysis, productivity monitoring, absence prediction, biometric attendance and AI-generated rankings. The decisive issue is whether a human can independently change the result—not merely whether a human technically makes the final decision.

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Police forces and public bodies

Document statutory authority, necessity, proportionality, watchlist construction, retention, deletion, operator training, demographic performance, auditability, challenge mechanisms and public communications. Public bodies must also consider policing law and human-rights obligations, not just data protection.

Developers and vendors

Prepare evidence about training-data provenance, lawful processing, security, retention, testing, subgroup performance, model changes and downstream controls. A label such as “AI compliant” does not transfer the customer’s legal responsibility.

Retailers, venues, healthcare and education

Facial recognition, identity verification, monitoring and inferential tools can affect access, safety, treatment or participation. Organisations should assess whether a less intrusive method could achieve the same purpose and whether people can realistically refuse or challenge the processing.

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What happens next?

The March 2026 update points to continuing work on ADM and profiling guidance, the statutory AI and ADM code, foundation-model policy positions, recruitment findings, police audits and agentic AI. The ICO says it published an emerging-technology report on agentic AI in January 2026; that is horizon-scanning, not a complete regulatory regime.

The original plan of action described targeted activity over 2025/26. Future legislation may also alter the framework, including rules for law-enforcement use of biometrics. Organisations should monitor developments, but should not postpone current compliance work while waiting for final guidance.

The practical takeaway

The ICO’s strategy changes the regulator’s emphasis more than it changes the basic legal starting point. If an organisation uses AI or biometrics involving personal data, it should be able to explain why the processing is lawful and necessary, how risks were tested, what safeguards operate, and how an affected person can obtain meaningful review.

That preparation is especially important for high-impact systems. The safest approach is to treat the strategy as a warning about regulatory attention—not as a future date on which compliance begins.

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