The Official Publication of the Ontario Association of Chiefs of Police 13 A rtificial Intelligence (AI) is no longer e x p e r i m e n t a l in policing – it is operational, embedded and increasingly inseparable from modern law enforcement infrastructure. Across Ontario, AI capabilities exist not only in investigative or analytical software, but directly within frontline hardware and core platforms, includ- ing body-worn cameras, in-car video systems, automatic licence plate recognition (ALPR), mobile devices, radios, network infrastructure, records management systems and cyber- security tools. In many cases, these capabilities are enabled by default through firmware updates, cloud sub- scriptions or vendor-managed services, often without the same visibility, scru- tiny or governance traditionally applied to standalone applications. For Ontario police leaders, the central question is no longer whether AI will be used, but how it can be governed responsibly, transparently and at scale. This requires a deliberate shift away from viewing AI as a discrete or emerging technology and toward managing it as an enterprise capability – one that spans applications, devices, data pipelines, vendors, procurement practices and people. Trustworthy AI in policing is therefore not a technical problem alone; it is an organizational, legal and governance challenge. This article examines how Ontario police services can operationalize F E A T U R E trustworthy AI through practical, enterprise-wide governance models. It explores how governance can be applied consistently across AI-enabled technologies regardless of form factor, and outlines approaches to policy alignment, risk assessment, approval and documentation, mon- itoring, accountability, workforce impacts and common pitfalls to avoid as AI becomes embedded across modern policing ecosystems. AI IS ALREADY EMBEDDED One of the most significant govern- ance challenges facing police services today is that AI is no longer confined to clearly labeled “AI systems.” Modern policing technologies increasingly include AI-driven capabilities such as automated transcription, video and audio redaction, object and behaviour detection, pattern rec- ognition, anomaly detection, threat scoring, predictive maintenance and adaptive cybersecurity controls. These capabilities are frequently delivered through: • Vendor-managed cloud platforms; • Firmware and software updates; • Embedded intelligence within devices; and • Machine-learning-based secur- ity and monitoring tools. Because these capabilities are often bundled into broader plat- forms, services may already be using AI in operational contexts without a Beyond the Hype: Operationalizing trustworthy AI in Ontario policing By Christine Robson, Durham Regional Police Service consistent understanding of where it exists, how it functions, what data it processes or what operational decisions it influences. Treating AI governance as an application-level issue is no longer sufficient. Instead, governance must account for AI wher- ever it appears – whether in evidence management, infrastructure or secur- ity tooling. FROM POLICY TO PRACTICE Operationalizing trustworthy AI requires governance that applies con- sistently across the entire technology lifecycle – procurement, deployment, use, monitoring and retirement – regardless of whether AI is delivered through software, hardware or man- aged services. Effective governance recognizes that if an AI capability can influence operational decisions, evidence or member behaviour, it must be visible, documented and accountable. Key elements of an effective enterprise AI governance framework include the following. 1. CLEAR POLICY ALIGNMENT AI governance must be anchored in existing legal, ethical and operational frameworks rather than treated as a separate or experimental domain. Effective AI policies should align with: • Privacy and access-to- information legislation; • Records management and evidentiary standards; • Professional standards and accountability frameworks; • Cybersecurity and information security policies; and • Procurement and vendor management practices. Importantly, AI policies should apply equally to internally developed tools, commercial platforms and vendor-embedded capabilities. This ensures consistent expectations regardless of where or how AI is introduced.
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