By: Brian Gilkey, Vice President North America, Decision Intelligence, Cognyte
Federal law enforcement agencies strive for one thing in their criminal investigations – faster data analysis. It yields faster time to actionable intelligence and faster case resolution. Analysts are swimming in investigative data today but procuring and implementing AI for automated analysis remain major challenges for federal agencies.
These are challenges of their own making. Long-ingrained processes and investigative methodologies manifested in procurement and workflow pipelines that confine valuable data into silos built around point solutions, creating islands of intelligence in an ocean of investigative data.
AI doesn’t thrive in this environment. It demands rich, connected, consolidated data sets to deliver its full analytical value. In this light, adopting AI and automated analysis was always going to be an uphill climb in investigative applications.
The agencies that overcome these challenges will make giant leaps in making their communities safer. The promise of AI is that profound.
WHAT’S THE HOLD-UP?
Federal law enforcement no longer needs convincing that AI will be essential to modern investigations. It’s a directive framed in U.S. federal policy. OMB Memorandum M-25-21 directs agencies to accelerate responsible AI adoption while maintaining public trust and applying risk-management practices to high-impact AI. It also calls on agencies to strengthen data governance, traceability, interoperability and security as they scale AI use.
AI automation is a known need among federal agencies. The risk is that AI adoption will continue moving at institutional speed while investigative data grows at machine speed.
Intelligence data today is successfully – but slowly – gathered with point tools. From mobile forensics tools that pull data from lawfully seized mobile devices to geo forensics tools like automated license plate recognition (ALPR), point solutions are often great at what they do but they aren’t designed to interact or integrate. Public records databases and OSINT (open-source intelligence) can likewise be brought to bear in agency investigations, but this data is consistently confined to silos.
Individually, these data sources are enormously valuable, but they can’t be understood in isolation. Investigators and analysts don’t have the background or bandwidth to master these point tools or synthesize these silos. A patchwork of manual processes is applied where AI automation should shoulder the burden.
According to a recent survey, more than half (53%) of law enforcement agencies say that their inability to access relevant data sources is the top technological pain point causing delays in resolving investigations. These delays can directly impact public safety.
Public-sector investment in advanced, AI-driven law enforcement technology is accelerating. Federal agencies know they need AI automation – and they’re prime candidates to exploit AI and ML models for meaningful benefit.
There’s one last hurdle agencies must overcome. What’s missing – and what’s needed – is trust.
There’s a lingering resistance to deploying AI tools that handle sensitive, high-stakes data, and in particular concerns pertaining to Explainable AI – the ability to understand how an AI-generated lead was reached and trace it back to source data so investigators can validate it under legal and regulatory scrutiny.
The G7 Interior and Security Ministers, including the United States, adhere to law enforcement principles that stipulate automated AI processes should generally be “human interpretable” and “logically reconstructable.” Crucially, these principles affirm that automated processes should support rather than replace human decision-making.
A modern, purpose-built, AI-driven decision intelligence platform can fuse and analyze decentralized, unstructured data – including mobile forensics and geo forensics data – and allow analysts to look deeply into massive data sets to verify where investigative data was sourced and confirm that the source itself is reliable. This attribution capability is essential – and it’s achievable today.
Security standards are likewise in place today governing how and where this data can be processed by AI analytics. PII (Personally Identifiable Information) and CJI (Criminal Justice Information), for example, are tightly controlled per defined security policies and mandates, and these systems have traditionally been deployed on premises.
Vast, well-defined, fenced-in investigative data sets like these are perfect environments for AI analysis to thrive. Here AI doesn’t reach beyond the guardrails, and it doesn’t interact with the web where data distortions can introduce hallucinations. AI can be trusted to conform to strict security standards; the security perimeter is non-negotiable.
Under these tightly controlled conditions, AI safeguards are built in. Additional safeguards are put in place by analysts themselves. There’s accountability – explainability – at every stage in the workflow, and human experts never lose oversight.
AUTOMATION WITH ACCOUNTABILITY
Federal law enforcement agencies increasingly recognize that AI is essential to keeping pace with the investigative data deluge. What’s needed is the institutional urgency to turn that recognition into operational reality, and decision intelligence platforms that unlock the value of point tools in a unified way.
Agencies can preserve strict data security, maintain attribution to source intelligence and keep human experts firmly in control while giving machines the work humans can’t perform at scale, searching millions of records, connecting disparate data points and surfacing relationships at speeds no analyst could approach.
This approach lets expert investigators do what they do best – interrogate the findings, follow the evidence, apply context and judgment, and make informed decisions that accelerate case resolution.
The technology and safeguards are ready. Procurement practices and investigative workflows must now catch up.
Brian Gilkey, Cognyte
Brian Gilkey is a prominent figure in the homeland security, law enforcement and intelligence analytics space. He is currently the Vice President, North America at Cognyte. He has over 25 years of experience in the intelligence, investigative technology and threat-assessment world. He regularly collaborates with North American law enforcement command staff, federal agencies and multi-agency task forces to modernize investigations and intelligence gathering. His expertise includes counterterrorism, cross-border human trafficking interdiction, narcotics/weapons smuggling analysis and intelligence-led major event security planning.