GITNUXSOFTWARE REVIEW
Public Safety CrimeTop 10 Best Gun Detection Software of 2026
Compare gun detection software options like ZeroEyes, Actuate AI, and Vaidio to match use cases, integrations, and deployment needs.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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ZeroEyes is the best pick for SOC teams that need automated firearm alerts across camera networks because its confidence-gated workflow routes events to operator review to manage false positives, whereas Panic Technology Gun Detection fits teams that want workflow-first escalation with built-in human verification around verified incidents.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
ZeroEyes
Confidence-gated alerting tied to an operator review workflow to manage false positive rate during real-world camera scenes.
Best for: Fits when a security operations center needs automated gun detection alerts plus confidence-based operator review across camera networks.
Actuate AI
Editor pickHuman-in-the-loop review workflow tied to event routing decisions.
Best for: Fits when SOC teams need firearm detections routed through review before escalation.
Vaidio
Editor pickIncident workflow links gun detection events to a review stage for confirmation before escalation.
Best for: Fits when security teams need firearm classification with review workflow and controlled escalation, not just model output.
Comparison Table
ZeroEyes
enterpriseAI video analytics identify visible firearms and route alerts for human verification.
Confidence-gated alerting tied to an operator review workflow to manage false positive rate during real-world camera scenes.
ZeroEyes performs automated firearm detection and alerting from IP camera feeds using computer vision and real-time event logic. The system focuses on incident escalation workflows that connect detection confidence with operator review so security teams can manage false positive rate and false negative rate tradeoffs.
ZeroEyes supports network camera integration and can run in cloud or on-premises-like deployments depending on the deployment model selected. The product is designed for security operations center monitoring and event-driven response across multiple cameras.
- +Configuration supports multiple camera integrations for consistent detection coverage
- +Real-time alerting logic includes detection confidence gating for operator workflows
- +Incident escalation supports human-in-the-loop review to reduce unnecessary dispatch
- +Supports export or relay of event data to downstream security workflows
- –Requires careful camera placement and scene tuning to control false alarms
- –Integration depth varies by video management system and event ingestion path
- –Operator review workflows can add overhead during high-traffic periods
- –Deployment shape adds governance work when scaling across many sites
Security operations center teams
Escalate firearm alerts after operator review
Fewer unnecessary high-priority calls
Physical security managers
Tune confidence to balance misses and false alarms
More manageable alert queues
Show 1 more scenario
Network video operators
Monitor multiple IP cameras in one workflow
Faster incident handling
The system ingests camera feeds and applies real-time event logic to route alerts across sites.
Best for: Fits when a security operations center needs automated gun detection alerts plus confidence-based operator review across camera networks.
Actuate AI
enterpriseAI gun detection software that integrates with existing IP camera systems to identify firearms and alert security teams in real time.
Human-in-the-loop review workflow tied to event routing decisions.
Actuate AI is positioned for teams that need firearm detection plus an operational pipeline for incident handling. Detection events can be reviewed by humans, and outcomes can be forwarded into escalation workflows that match SOC practices. Integration depth matters here because the value depends on how well the events fit into existing monitoring and response processes.
A practical tradeoff is that the strongest results require deliberate camera and workflow configuration to control false positives and escalation quality. It is a good fit when a security team already runs centralized monitoring and wants to add weapon detection with review gates, not just raw alerts.
- +Human review step reduces escalation noise
- +Integration and automation hooks fit existing SOC routing
- +Workflow-oriented event handling supports incident lifecycle
- +Configuration options help align detection with coverage goals
- –Workflow tuning can take time to reach stable alert quality
- –Camera onboarding and validation require operational discipline
- –Advanced automation depends on correct API event mapping
- –Edge-to-cloud or deployment specifics can affect integration effort
Security operations analysts
Review firearm alerts before escalation
Cleaner escalations and faster triage
Security engineering teams
Automate event handling via integrations
Lower manual processing
Show 1 more scenario
Physical security managers
Standardize detection across sites
Consistent incident outcomes
Configuration supports repeatable camera and workflow patterns across multiple locations.
Best for: Fits when SOC teams need firearm detections routed through review before escalation.
Vaidio
enterpriseAI video search and analytics include firearm and weapon detection capabilities.
Incident workflow links gun detection events to a review stage for confirmation before escalation.
Vaidio targets firearm detection workflows with video analytics built for security operations and investigation. The system supports computer vision driven classification that routes incidents into review and escalation steps rather than treating every alert as a final decision.
Administrators can tune detection behavior and manage where analytics runs to fit different network and camera layouts. Human-in-the-loop review is positioned as part of the workflow to reduce detection errors before downstream escalation.
- +Human-in-the-loop review supports safer incident escalation than auto-dismissal
- +Configurable detection behavior helps balance false alarms against missed events
- +Incident workflow supports investigation handoff beyond raw detections
- +Deployments can fit varied camera networks without forcing a single VMS model
- –Tuning detection thresholds takes iteration to reach stable performance
- –Coverage depends on camera placement and image quality for reliable detection
Video analytics administrators
Validate detection relevance with operator feedback
More consistent review practices
Security operations centers
Triage firearm detections before escalation
More controlled alert handling
Show 2 more scenarios
Security and operations staff
Review daily alert events through workflow
Structured daily triage
Operators process routine person, vehicle, behavior, motion, and safety events via repeatable steps.
Physical security teams
Investigate after-hours intrusions consistently
More repeatable investigations
Teams examine person and motion events tied to restricted periods to verify suspected intrusions.
Best for: Fits when security teams need firearm classification with review workflow and controlled escalation, not just model output.
Omnilert Gun Detection
enterpriseComputer vision detects visible firearms across connected video surveillance systems.
Human-verification driven escalation links firearm detection events directly into Omnilert incident routing.
Omnilert Gun Detection centers on turning firearm detection outputs into actionable notifications and an incident sequence managed in Omnilert. The workflow supports verification steps so operators can reduce unnecessary escalations when confidence is low or the scene is ambiguous. The integration story is strongest for teams already using Omnilert for alerting, because the detection events can flow into the same escalation governance.
- +Incident escalation connects firearm alerts to operational response workflows
- +Configurable verification steps reduce unnecessary escalations
- +Works well for teams standardizing alert routing across sites
- +Human review triggering supports tighter false-alarm handling
- –Alert volume depends heavily on camera coverage and sightlines
- –Workflow design requires clear ownership of review versus escalation
- –Limited standalone value if Omnilert alerting is not already in use
- –Tuning detection confidence thresholds takes time for each environment
Security operations teams
Triage firearm alerts from monitored cameras
Fewer unnecessary dispatches
Multi-site security managers
Standardize alert handling across locations
Consistent incident response
Show 2 more scenarios
On-call response teams
Trigger escalation with clear timing
Faster confirmed response
Escalation sequences route potential firearm events to on-call staff with defined delays.
Security administrators
Govern notification and review responsibilities
Improved accountability
Configuration separates review actions from escalation actions to enforce operational controls.
Best for: Fits when centralized security teams need firearm alerts to trigger governed escalation workflows.
Panic Technology Gun Detection
vertical specialistAI-driven gun recognition software that integrates with existing CCTV infrastructure.
Human-in-the-loop review and escalation workflow is built around operator verification, not inference-only alerts.
Panic Technology Gun Detection is positioned for security operations where detections must be reviewed, logged, and acted on with low operational friction. The workflow emphasis favors teams that manage incidents through a central monitoring station and require predictable handling of detection confidence. Deployment flexibility supports environments that cannot rely solely on cloud inference for all video traffic.
- +Workflow-first alert handling with human verification steps
- +Deployment flexibility for on-premises or hybrid environments
- +Event routing supports operational escalation patterns
- +Designed for incident operations, not just raw detection output
- –Requires careful camera coverage validation to control false positives
- –Tuning detection thresholds and workflows takes operational time
Security operations centers
Central alert verification workflow
Faster verified incident handling
Multi-site security teams
Consistent events across locations
Lower variance in responses
Show 1 more scenario
On-premises video operators
Hybrid processing with local constraints
Reduced external video dependency
Runs video detection workflows in environments that require local processing paths.
Best for: Fits when security teams need repeatable escalation from gun detections to verified incidents.
BastionZone
SMBReal-time gun detection software that connects to existing CCTV systems to identify firearms in camera feeds.
Human-in-the-loop review queue that pairs detection results with analyst validation before escalation.
BastionZone performs firearm detection from monitored camera feeds and routes alerts into security workflows. It focuses on human-in-the-loop review so analysts can validate detection confidence before escalation.
- +Human-in-the-loop review supports analyst validation before escalation
- +Camera connection management supports ongoing monitoring with fewer manual steps
- +Configurable alert rules reduce noise in the incident stream
- +Workflow alignment targets control-room handling of detections
- –Tuning detection behavior can require iterative configuration per site
- –Integration depth with VMS and event systems is limited compared with top-tier vendors
- –Alert payload detail may be insufficient for deep automated enrichment
- –Throughput scaling for large camera counts is not as clearly documented as leaders
Security operations analysts
Validate firearm detections before incident handoff
Lower risk escalation
Control room operators
Apply configurable firearm alert rules
Consistent incident routing
Show 1 more scenario
Security administrators
Tune detection behavior per camera setup
Better fit to site cameras
Admins manage camera connections and adjust detection behavior for the deployment’s video layout.
Best for: Fits when security teams need human-validated firearm alerts with practical control-room workflows.
Xtract One
vertical specialistWeapons screening systems detect concealed firearms and other threats at entry points.
Configurable operator review queues that attach detection confidence context to firearm classification alerts.
Xtract One focuses on gun detection workflows built around computer vision inference and human-in-the-loop review to manage firearm classification outcomes. The solution routes detections into configurable alerting and incident escalation so teams can triage cases using detection confidence and review cues. Video ingestion supports common IP camera paths used in security operations, and integrations target typical VMS and monitoring station deployment patterns.
- +Human-in-the-loop review helps reduce false positives during firearm classification triage.
- +Configurable alert escalation supports repeatable incident handling for security teams.
- +Works with common IP camera streaming paths used in physical security networks.
- +Detection confidence signals help operators prioritize events for faster review.
- –Fine tuning detection thresholds can take iterative setup for specific camera views.
- –Governance and audit reporting depth for RBAC workflows is limited for enterprise rollouts.
Security operations center staff
Triaging firearm cases using confidence cues
Faster triage decisions
Security analysts
Escalate weapon detections after review
Reduced missed escalations
Show 1 more scenario
Video management operators
Ingest IP camera streams for inference
Lower integration friction
The solution ingests common IP camera paths and supports monitoring station style deployments.
Best for: Fits when security teams need firearm detection with operator review and predictable alert escalation.
Athena Security
vertical specialistVideo analytics identify weapons and other security threats in monitored environments.
Confidence-scored firearm classifications designed for analyst review routing, so alerts can be gated before incident escalation.
Athena Security targets security operations teams that need firearm detection workflows tied to existing camera and alarm escalation processes. The core capability is computer vision-based firearm classification that produces per-clip detections with confidence signals for human-in-the-loop review.
- +Human review workflow reduces firearm misclassification before escalation
- +Confidence-scored detections support triage and incident prioritization
- +Integration-oriented alert outputs fit incident workflows
- +Firearm classification outputs map to handgun and rifle targeting needs
- –Tuning for detection confidence can require iterative calibration
- –Operational governance depends on teams setting consistent review rules
- –Camera integration scope may require specific VMS or feed standards
- –Latency behavior can vary with edge or cloud inference placement
Security operations teams
Route per-clip firearm detections to investigation
Better investigation prioritization
Security incident handlers
Escalate detections without stopping at analytics
More actionable alerts
Show 1 more scenario
Video operations administrators
Run detection with end-to-end video handling
Fewer workflow handoffs
The deployment model supports managing feeds and incident handling together for firearm workflows.
Best for: Fits when a security operations center needs firearm detections routed into review and escalation workflows with confidence-based triage.
Evolv Technology
enterpriseAI-based weapons detection screening system designed for high-traffic venue entrances.
Human-in-the-loop verification that routes low-confidence firearm detections into reviewer queues for consistent incident escalation.
Evolv Technology focuses on gun detection and firearm classification from live video feeds, with workflow review built for security teams that need controllable false positive rates. The system supports human-in-the-loop review for detections that exceed configured confidence thresholds, which helps teams route ambiguous events into investigation queues.
- +Human-in-the-loop review paths reduce uncertainty in weapon detections
- +Firearm classification supports separate handling for likely handguns and rifles
- +Governable alert routing supports consistent escalation rules across teams
- +Video feed integration supports centralized monitoring station workflows
- –Achieving stable detection confidence depends on camera coverage and viewing angles
- –Review queues require operational discipline to prevent backlog during high alert volume
- –Automation breadth for custom event schemas can be limited compared with extensible API-first tools
- –False negative and latency tuning can take multiple iterations on each site
Security operations analysts
Queue ambiguous firearm events for review
More consistent verification
Security governance leads
Control reviewer permissions across shifts
Lower variance in decisions
Show 1 more scenario
Video management teams
Feed detections into central incident escalation
Unified monitoring pipeline
An integration layer connects camera and video management workflows to central monitoring and escalation.
Best for: Fits when security teams need classification-led gun alerts with controlled human review for investigations.
Ambient.ai
enterpriseComputer vision analyzes camera feeds for weapons and security incidents.
Review-first firearm detection pipeline that routes detections into escalation with configurable event conditions.
Ambient.ai focuses on firearm detection workflows that fit into existing security operations with video analytics and event-driven alerting. The core capability is computer-vision based detection of firearms with reviewable detections that security teams can route into incident escalation.
- +Event-driven firearm detections designed for incident escalation workflows
- +Human-in-the-loop review fit for managing detection confidence and false alarms
- +Camera and VMS integration approach supports centralized monitoring station setups
- +Configurable alert conditions help tune detection behavior across sites
- –Setup requires careful camera view calibration to avoid missed detections
- –Governance controls and audit logging depth are less transparent than in top-ranked options
- –Detection performance can vary sharply with low-light scenes and occlusion
- –API extensibility for custom routing is narrower than some comparable vendors
Security operations teams
Verify weapons from monitored camera feeds
Faster verification and escalation
Healthcare facilities teams
Detect unsafe events alongside firearm workflows
Earlier safety awareness
Show 2 more scenarios
Campus perimeter security
Track restricted-area and trespass activity
More consistent perimeter detection
Teams identify perimeter breaches and unauthorized entry across monitored sites using camera events.
Forensic investigators
Find relevant footage using detection metadata
Faster evidence review
Investigators search for relevant moments using AI detection metadata rather than manual timeline review.
Best for: Fits when security teams need firearm detection with reviewable events and incident-ready escalation.
Conclusion
After evaluating 10 gun detection software, ZeroEyes stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Gun Detection Software
How does Omnilert Gun Detection route firearm detections into an operations workflow instead of ending at a model output?
Which tool is better when human-in-the-loop verification must gate escalation based on operator review, not model confidence alone?
How should security teams compare Omnilert Gun Detection versus ZeroEyes for reducing false positives in real camera scenes?
When does on-premises or hybrid processing matter, and which platform supports it explicitly?
How do Actuate AI and Ambient.ai differ in the way they connect gun detection events to existing operational systems?
Which product is designed around incident workflow links that route detections to a review stage before escalation?
What breaks if an organization skips governance controls for reviewer permissions during verification?
How should teams handle detection latency when multiple cameras feed a SOC or central monitoring station?
Where do governance and administration differ most when teams need to tune detection behavior per deployment?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Gun Detection Software
In gun detection software, options separate by how well they fit your workflow: whether detection results stay explainable and actionable, whether alerts can be tuned to reduce nuisance calls, and whether your team can manage rollout using RBAC, audit logs, and automation APIs.
Alert tuning and suppression controls
Why it matters
Alert tuning and suppression controls determine whether the system stays usable during busy shifts. Without good knobs, you end up with too many notifications that teams ignore—or too few that miss events.
How to check
Ask to see controls for confidence thresholds, per-camera schedules, and suppression/holdoff windows. Then test with a short demo clip that includes both clear and ambiguous cases, and verify how alerts change when you adjust those settings.
Evidence package and explainability
Why it matters
Evidence package and explainability matter when you need to review what triggered an alert and why. If the system only notifies, you spend time reconstructing context after the fact.
How to check
In a demo, request an export that includes the alert trigger time and a short clip or frames around the event. Confirm whether the export includes any human-readable reason (for example, the configured trigger condition) or only a status label.
Nuisance-event diagnostics
Why it matters
Nuisance-event diagnostics keep operations grounded in what’s driving noise. They help you separate camera/site issues from model behavior when false alerts happen.
How to check
Ask how the product surfaces top causes for recent alerts (for example, scene conditions, angles, or partial views). Then run a controlled test with a few camera views you know are challenging and see whether the dashboard highlights the likely reason and suggested adjustments.
Automation API and event webhooks
Why it matters
Automation API and event webhooks reduce manual work and keep incident handling consistent. If teams can’t connect alerts to existing processes, the system becomes another notification stream.
How to check
Ask for a sample workflow that sends an alert payload to your endpoint, including camera ID, time, and evidence links. Review the webhook schema documentation and test that you can filter by location, device, or alert type.
RBAC and audit log for changes
Why it matters
RBAC and audit log for changes protect operational settings as more people touch configuration. When you can’t track who changed thresholds or schedules, it’s hard to learn from incidents.
How to check
Request a role setup walkthrough: separate a viewer from an admin and verify which actions are blocked. Then change a threshold or schedule in a demo and confirm you can find the change in the audit log with timestamp and actor.
Red flags
- Alert tuning options are limited to one global switch
- Exports show clips without any usable trigger context
- Alerts connect poorly to automation, requiring manual copying
Fit by situation
- If you run day-and-night shifts with different camera views, prioritize alert tuning and suppression controls.
- If investigators need quick context for follow-up, prioritize evidence package and explainability.
- If you must route alerts into existing workflows automatically, prioritize automation API and event webhooks.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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