AI and Police: Strict Governance and Dedicated Tools

An investigation into the alleged misuse of AI within the Derbyshire police is part of a broader review of generative tools in the UK. Beyond this case, the handling of sensitive evidence imposes rules of traceability, secure environments, and clear usage limits to avoid cascading effects on evidence and justice.

Proving Usage: Defensibility, Oversight, and Restricted Areas

Trust in the results produced with the help of AI depends as much on how they are obtained as on their content. As these tools become integrated into case processing, organizations will need to demonstrate that they are used fairly, proportionately, and under appropriate supervision. Technical controls alone are not sufficient. A governance framework must explicitly outline the areas where AI should not intervene: certain activities, particularly those related to judicial procedures, protection, or individual rights, are not suitable for automated assistance. Defining this boundary is as important as choosing the platform. The goal is to ensure an integrity that can withstand scrutiny in a courtroom or public inquiry.

Unmanaged Uses: Errors That Contaminate Decisions

The failures observed stem from unmanaged and unauthorized uses, not from the technology itself. Therefore, it is essential to eliminate inappropriate uses rather than AI as a whole. Despite advancements, generative systems still exhibit a margin of error: some models achieve hallucination rates of 4.2%, compared to 12.2% for earlier versions. These figures reflect increased reliability, but rigorous oversight remains essential. The generated results cannot be considered reliable without verification: they may be presented as factual while being out of context or based on erroneous analyses, and may be included in reports before any validation. When generated content is incorrect or unverifiable, its consequences ripple through subsequent actions, weakening prosecutions, delaying protective decisions, and providing opportunities for those targeted.

Internal Policies: Clarifying Tools, Uses, and Human Roles

The handling of sensitive, high-stakes information raises a governance issue. As AI becomes more useful, it is increasingly necessary to define the conditions for secure usage. Organizations must establish policies that identify authorized tools, specify permitted uses, and delineate the space where human judgment remains essential. In the absence of clear limits, individuals set their own rules, often under time pressure. A governance framework must also assign responsibilities related to assisted decisions, formalize the validation of results, and make explicit the usage limits for users.

Why Generalist AIs Clash with the Chain of Evidence

Intelligence and investigations require complete traceability and auditability: every conclusion must be traceable to its source, and every step must be defensible long after the facts. Most generalist AI tools are not designed to meet these requirements. Entrusting sensitive information to external services raises issues of security, compliance, and data sovereignty. Once documents leave a controlled environment, the organization no longer controls the storage location, possible accesses, or the potential for later use in training models.

What Specialized Investigation Platforms Change

AI finds its place in intelligence and investigation when integrated into specialized platforms designed to ensure auditability and the continuity of evidence from the outset. In these environments, AI accelerates time-consuming tasks by linking elements without making judgments. It can flag the presence of the same entity in intelligence reports, transaction records, and internal correspondence, submitting these links for an agent's assessment. Patterns can thus emerge in minutes rather than after weeks of manual cross-referencing, with the final decision remaining human. These platforms operate in secure environments, restoring organizations' control over the storage and processing of sensitive data. Queries, results, and decisions are documented, facilitating the demonstration of accountability in case of disputes.

British Context: Operational Pressure and Scope of Use

An investigation into the alleged misuse of AI within the Derbyshire police is part of a broader review of generative tools used by British police, with implications that extend beyond this single service. Particularly effective tools are emerging in institutions where sensitive data underpins decisions that may require subsequent justification. The goal is not to ban AI from investigative or intelligence activities but to integrate it into secure and auditable frameworks, accompanied by precise restrictions and human oversight, to ensure that every output is traceable, understandable, and justifiable. The danger arises when, faced with an increased workload and a dispersion of data among numerous teams, agents resort to generalist solutions that do not meet security and evidentiary requirements. Tools capable of condensing reports in seconds are attractive, but if they are not approved and remain outside the security perimeter, without traceability of queries and outputs, they may omit essential elements, which is unacceptable when guilt, innocence, and lives are at stake. The risk then concerns the integrity of evidence, compliance, and the administration of justice. The recent police review does not question the value of AI for these missions; benefits are expected in secure and audited environments, where usage remains defensible.