Public Safety Object, Explosive, and Track Threat Detection
AI/ML workflow for law enforcement and public-safety teams that detects suspicious objects or explosives in imagery/video and analyzes large-scale air and maritime tracks to surface near-real-time suspect activity alerts without overwhelming operators.
Business Blueprint
GROUNDEDAI threat detection that turns high-volume public-safety imagery, sensor, and track data into timely suspect-activity alerts for human operators.
The Problem
Public-safety and law-enforcement teams need to detect suspect activity across overwhelming streams of operational data quickly enough to act, without forcing operators to manually evaluate every signal.
AMOC officers and watch officers
They must monitor massive national air and maritime sensor volumes, including between 180,000 and 200,000 individual tracks and five to ten terabytes of streaming data daily, while needing real-time suspect-activity reporting.
HSI investigators
They need AI-supported investigative tools to counter fentanyl flows and support operations that identify victims and suspected perpetrators.
Process Fit
Security operationsAs-Is
Operators and investigators monitor large volumes of sensor, track, and case-related data to find suspect activity, but the volume and time sensitivity make manual review hard to sustain.
To-Be
AI/ML analytics evaluate the operational data stream, flag suspect patterns, and publish structured alerts directly into existing operator screens or investigative workflows so humans can decide the response.
Human Checkpoints
- Review the suspicious-activity message displayed on the existing watch-officer screen before taking operational action. — Watch officer
- Validate and act on AI-supported investigative leads during enforcement operations. — HSI investigator
Systems Touched
Business Cycle
Upstream
- Operational sensor and contextual data must be available for continuous processing and enrichment.
- The operation needs defined suspect-activity patterns or analytic criteria that can be applied to movement, location, time, and other context.
Downstream
- Suspect activity is converted into structured messages and displayed directly to watch officers in their existing workflow.
- Detection and enforcement workflows can change downstream outcomes, including suspect-activity detection, seizures, arrests, and victim identification/rescue.
Value Evidence
- Suspect-activity detection rateINCREASED
In fiscal year 2023, these analytics drove a 500% greater rate of suspect activity detection
- Suspicious-activity report latencyIMPROVED
only ten seconds or less elapse between the time data is received and the time KestreI delivers a suspicious activity report
- Air and maritime tracks evaluatedINCREASED
At any given time, there are between 180,000 and 200,000 individual air and maritime tracks
- Streaming data processedINCREASED
continuously process five to ten terabytes of streaming data daily
- Seizures and arrestsINCREASED
contributed to a 50% increase in seizures and 8% increase in arrests
- Victim identification and rescueIMPROVED
resulted in the identification and rescue of 311 previously unknown victims of sexual exploitation from active abuse and the arrests of suspected perpetrators
- Narcotics seizedINCREASED
had a couple of significant busts where $81 million of narcotics were seized.
ROI Estimator
EstimateKPI
Seizures and arrests
Projected Annual Change — Seizures and arrests
—
Based on observed result at 1 operator — verify against your own baseline.
Adoption Journey
LEVEL 1 — QUICK WIN
Gate: Prove value on one high-priority threat feed or use case with human review of every alert.
Outcome: A focused quick win that shows operators whether AI-assisted triage can surface useful suspect activity without changing the whole command-center workflow.
LEVEL 2 — STANDARD
Gate: Prove production readiness inside approved government systems and existing operator screens.
Outcome: A production alerting workflow where suspect-activity reports reach watch officers fast enough for operational decisions.
LEVEL 3 — ADVANCED
Gate: Prove the workflow can scale across national-scale feeds, multiple threat patterns, and sustained daily data volumes.
Outcome: Scaled coverage across large air, maritime, or comparable public-safety streams, with operators able to evaluate far more activity than manual monitoring alone would allow.
Detailed per-level builds in the solution spectrum below
Risk & Governance
Inaccurate or nonsensical AI outputs could create unsafe public-safety alerts.
Posture: Prefer bounded statistical, heuristic, and spatiotemporal analytics for operational alerting where appropriate, because the deployment notes these consume less compute and do not introduce the same hallucination risks.
Sensitive national sensor and law-enforcement data must be handled in approved environments.
Posture: Run the workflow in approved government cloud and enterprise platforms, as observed with AWS GovCloud development/testing and approval to go live in CBP’s CACE enterprise platform.
Alerting must not create a second screen or separate workflow that operators ignore.
Posture: Publish structured suspect-activity messages directly onto the watch officer’s existing screen.
AI-generated suspect flags can affect enforcement decisions and therefore need human accountability.
Posture: Keep officers and investigators responsible for reviewing alerts, validating leads, and deciding any operational action.
Operating Intelligence
How it works
AI watches every signal continuously.
Humans investigate what it flags.
False positives train the next watch cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Observe
Step 2
Classify
Step 3
Route
Step 4
Exception Review
Step 5
Record
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.
The Loop
6 steps
Observe
Continuously take in operational signals and events.
Classify
Score, grade, or categorize what is coming in.
Route
Send routine items to the right path or queue.
Exception Review
Humans validate flagged edge cases and adjust standards.
Authority gates · 1
The system may not initiate enforcement, interdiction, evacuation, or field deployment without approval from authorized public-safety personnel. [S1][S2]
Why this step is human
Exception handling requires contextual reasoning and organizational judgment the model cannot reliably provide.
Record
Store outcomes and create the operating audit trail.
Feedback
Corrections and outcomes improve future performance.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Public Safety Object, Explosive, and Track Threat Detection implementations:
Key Players
Companies actively working on Public Safety Object, Explosive, and Track Threat Detection solutions:
Real-World Use Cases
Kestrel AI threat-alert analytics for air and maritime monitoring
Kestrel watches huge streams of aircraft and vessel movement data and quickly flags movement patterns that look suspicious so human watch officers can investigate.
AI/ML object and explosive detection for law enforcement
AI helps detection systems recognize dangerous objects or explosives so law enforcement can find threats more quickly.