The recent watchdog investigation into alleged AI misuse in the UK by Derbyshire Constabulary, alongside wider scrutiny of generative AI use in policing, has intensified debate around how artificial intelligence should be deployed in investigations. These developments highlight an important point: the challenge is not AI itself, but how it is governed.
Recent guidance advising police forces in England and Wales to pause the use of generative AI for certain activities demonstrates just how seriously concerns around accuracy, accountability and evidential reliability are being taken.
As investigators face growing caseloads, fragmented data and limited resources, AI will inevitably become an attractive way to save time and improve efficiency. However, when officers use unauthorised tools, or deploy AI without clear policies, oversight and safeguards, the risks extend beyond operational challenges to evidential integrity, public confidence and the administration of justice.
To ensure the right balance is struck, police forces need defined policies outlining which AI tools are approved, what they can be used for, and where human judgement must remain central. Without this clarity, the risks are not just operational but evidential.
In investigative environments, errors can affect real-world justice outcomes, from prosecutions and safeguarding decisions to the disruption of criminal networks. With the right governance in place, AI can strengthen investigations without compromising the integrity they depend on.
Why generalist AI tools can undermine evidential integrity
Investigations rely on clear audit trails, provenance and accountability; requirements that many generalist AI tools were not designed to support.
Entering sensitive intelligence into third-party platforms can create security, compliance and data sovereignty concerns. Once data leaves an organisation’s controlled environment, it may be exposed to risks such as unauthorised access, data loss, misuse in AI training models, cross-border data transfers and breaches of industry or regulatory requirements.
AI tools can also generate information that is incorrect or entirely fabricated, commonly referred to as “hallucinations”. In an investigative context, the risk is not simply that these errors occur, but that they can appear authoritative enough to enter reports, intelligence assessments or operational decision-making without being challenged.
A recent example emerged when the UK’s Home Office found that AI-generated content had been used in a report commissioned by West Midlands Police examining incidents involving Maccabi Tel Aviv supporters in Amsterdam. The committee concluded that AI had been used in ways that reinforced false narratives, raising concerns about the reliability, transparency and verification of the information presented.
The issue was not simply that AI had been used, but that questions were raised about how its outputs were validated, scrutinised and incorporated into the final analysis. The case highlights a wider challenge with generalist AI tools. While they can produce convincing summaries and assessments, they can also introduce inaccuracies, bias or unsupported conclusions that may not be immediately obvious to users.
When AI-generated information is wrong, incomplete or cannot be verified, it can lead to poor decisions during investigations, weaken cases and ultimately affect justice outcomes. This can impact everything from successful prosecutions and safeguarding efforts to disrupting criminal activity and resolving cases quickly.
It is important to note, though, that these risks do not mean AI has no place in investigations. Instead, they show why generalist tools, used without clear controls, are poorly suited to environments where evidence must be secure, traceable and defensible.
The opportunity lies in using AI through systems designed around investigative workflows, where efficiency is supported by governance, oversight and accountability.
The case for purpose-built AI investigations
Specialised investigative AI can help teams identify connections, analyse large datasets and prioritise intelligence more efficiently.
Unlike generalist tools, purpose-built platforms are designed around auditability, governance and evidential continuity. AI should enhance investigative expertise by accelerating data triage and surfacing insights, rather than replacing human decision-making.
For example, AI may highlight that the same individual, bank account or organisation appears across intelligence reports, transaction records and internal communications. By bringing these connections together, investigators can establish potential relationships and patterns of activity far more quickly than would be possible through manual review alone, while retaining full oversight of the conclusions drawn
Purpose-built platforms are also designed to operate within secure environments, providing greater control over how sensitive information is stored, accessed and processed. This allows organisations to benefit from advanced AI capabilities while maintaining compliance with legal, regulatory and operational requirements.
Rather than introducing additional risk, these systems can strengthen investigative processes by making it easier to document evidence, track decisions and demonstrate accountability throughout an investigation.
Building trust through accountability and transparency
Public confidence relies on knowing that investigations are carried out using processes that are secure, transparent and trustworthy, not just on the result. As AI becomes more embedded in investigative workflows, organisations must be able to demonstrate that its use is fair, transparent and subject to appropriate oversight.
This requires more than technical controls alone. Governance frameworks should clearly define responsibility for AI-assisted decisions, establish processes for validating outputs and ensure investigators understand both the capabilities and limitations of the technology they are using.
Governance should also establish clear boundaries around where AI can and cannot be used. Not every investigative activity is appropriate for AI assistance, particularly where decisions may directly affect legal proceedings, safeguarding outcomes or an individual’s rights. Defining these boundaries is as important as the technology itself.
Regular auditing, documented decision-making and clear lines of accountability are essential to maintaining confidence in AI-supported investigations.
When organisations can demonstrate that AI is being used responsibly and transparently, they are far more likely to secure the trust of investigators, regulators and the wider public. In this sense, governance is not simply about risk mitigation; it is a prerequisite for sustainable AI adoption.
The future of investigations will depend on combining technological innovation with robust safeguards that protect evidential integrity and public confidence.
Innovation and integrity must advance together
AI has the potential to transform investigations, helping teams manage growing volumes of intelligence, analyse complex datasets and respond more effectively to evolving threats. As organisations continue to explore its capabilities, the key challenge is how AI can be adopted in a way that maintains security, evidential integrity and public trust. Recent scrutiny has shown that the risks often stem from unmanaged or unauthorised use, rather than the technology itself.
The organisations that gain the greatest value from AI will be those that deploy it within secure, auditable environments that support investigative decision-making while preserving transparency and compliance. Innovation and integrity must advance together, ensuring every insight, decision and outcome can be trusted, explained and defended.
