There's no doubt that it was the talk of the town in New Orleans at GSX 2025. Forward-thinking enterprise security teams are using AI-powered video analytics inside their VMS and GSOC to automatically detect, validate, and triage events, then trigger guided workflows (a.k.a. “agentic” tasks) that speed response and reduce operator fatigue.
Results: fewer missed alarms, faster time-to-action, clearer audit trails, and tighter collaboration with IT and facilities.
A quick story: from noisy alarms to a clean, automated response
At 8:12 a.m., the corporate campus’ east gate shows a “tailgating suspected” alert. In the past, the SOC would get a motion alarm, hunt for the right camera, scrub video, and call facilities. Today it’s different:
AI detection: The VMS analytics flag a vehicle plus two persons crossing the badge-controlled lane within a 3-second window—confidence 92%.
Auto-validation: The system auto-pulls the adjacent LPR and card access events, correlating the plate and badge to known contractors. The badge read shows a single credential; analytics still see two bodies.
Agentic workflow: A prebuilt “Tailgating Response” workflow launches automatically:
Opens a 30-second multi-cam clip (entrance, side lane, overview).
Sends a push notification to the on-site security staff with the video clip.
Triggers a nearby public-address speaker with a courteous, pre-approved message: “Please badge in individually.”
Close-out: The n-site staff confirms compliance on their mobile app. The incident card is stamped with times, video, and acknowledgments, then filed to the case system for metrics.
The entire loop, from detection to corrective action, takes 45 seconds, and the SOC never leaves the single incident view. That’s AI doing what humans don’t do well at scale: watching, correlating, prioritizing, and guiding the next best step.
What “agentic AI” means for enterprise physical security
Agentic AI combines real-time analytics with decision logic and workflow automation. Instead of just labeling pixels (“person,” “vehicle”), it initiates a defined sequence, opening the right views, gathering evidence, notifying the right staff, and documenting actions. Think of it as a smart co-pilot for your GSOC.
Key building blocks:
Modern analytics: Object/attribute detection, object counting, speed, loitering, PPE detection, directional movement, restricted-area entry, gun/weapon detection (when approved and appropriate), and more.
Signal fusion: Video metadata + access control + LPR + intercom + intrusion + RTLS + OT sensors (e.g., gates, PLCs) + environmental alerts.
Workflows & playbooks: Policy-driven sequences with if/then branching. (Example: “If confidence > 85% and time is outside shift hours, page supervisor and show last 5 minutes from cameras A/B/C.”)
Feedback loop: Operators can confirm/override events; the system learns which alerts are signal vs. noise, improving future triage.
Top 5 Reasons Why enterprises are prioritizing AI-first GSOCs now
1) Measurable operator efficiency
1:Many camera coverage becomes practical because the AI watches first, humans decide last.
Incident timelines, evidence packages, and audit trails compile themselves.
2) False-alarm reduction
Analytics filter out weather, shadows, and routine operations.
Correlation with non-video systems reduces “single-sensor surprises.”
3) Faster, safer responses
Playbooks take the “now what?” out of the equation.
Integrated voice communication (intercom/PA/SMS) speed de-escalation and wayfinding.
4) Better executive reporting
KPI dashboards (alarm loads, response adherence) enable staffing and budget decisions based on data, not gut feel.
5) Alignment with IT (zero-trust, hybrid)
Modern platforms support on-prem + cloud recording/metadata, strong identity, encryption at rest/in transit, and role-based access.
Common, high-value use cases (and how AI helps)
Perimeter protection: Distinguish humans vs. animals vs. vehicles; escalate only when behavior matches risk (loitering near fence, approach from restricted zone).
Loading docks & yards: Detect truck arrival, assign bay, verify door lock state, and start a dwell-time clock that alerts when expected times are exceeded.
Lobby/turnstiles: Identify piggybacking/tailgating patterns; cross-check badge events; prompt guards with a friendly script.
Critical rooms (MDF/IDF, pharmacies, labs): Alert on entry outside approved windows; auto-attach access control logs and recent video for review.
Workplace safety: PPE detection, spills, blocked-egress, forklift/pedestrian separation, then route alerts to safety teams with annotated clips.
4 Key Takeaways to create an architecture that works in the real world
1) Start with the camera estate, but don’t get stuck there
High-quality sensors matter, but the real win is in consistent, metadata-rich streams (object class, attributes, confidence, track IDs). Even legacy cameras may be usable via analytic gateways.
2) Choose a VMS/PSIM that treats metadata as a first-class citizen - Look for:
Event bus that ingests/normalizes video and non-video signals
Contextual search that works like Google for incidents (e.g., “red truck near Gate 2 between 7–8 a.m.”)
Case management with evidence locking and chain-of-custody following Federal Rules of Evidence guidelines
3) Hybrid recording & compute
Keep mission-critical recording on-prem; leverage cloud for elastic analytics, long-term retention, or global search.
Use high-powered GPU where it matters (ingest points with dense analytics) and offload “good enough” tasks to edge devices like cameras.
4) Security & governance by design
Role-based access tied to corporate identity (SSO/MFA); least privilege.
Encryption (TLS 1.2+/FIPS-validated where required), signed firmware, and secure boot.
Data retention tagging by site and use case; automatic redaction for privacy exports.
Documented model governance: where models came from, how they’re tuned, false-positive targets, and revalidation cadence.
A sample implementation roadmap: from pilot to scale
Step 1 — Use-case workshop
Pick 2–3 high-impact pain points (e.g., tailgating, loading-dock dwell, after-hours restricted rooms). Define success metrics like “<10% false positives,” “<60s time-to-first-action.”
Step 2 — Data readiness & baseline
Inventory cameras, retention, network constraints, and current alarm volumes. Benchmark the “before” picture so ROI is provable.
Step 3 — Pilot projects in your environment
Pilot in a live area. Include:
Diverse lighting/weather scenarios
At least one integration (access control or LPR)
A playbook that actually executes something (notification, PA, door action)
Step 4 — Train operators on playbooks, not just UI
Give operators simple labels (“Confirm,” “Dismiss,” “Escalate”), and script their verbal responses when using intercom/PA. Collect their feedback weekly.
Step 5 — Scale and standardize
Template your runbooks, naming conventions, camera groups, and dashboard KPIs. Turn lessons learned into standards for new sites.
Buyer’s research checklist
Analytics quality: Can you see per-event confidence scores? Are there per-camera analytic profiles?
Runbook engine: Can events automatically open views, pull logs, notify roles, and create cases without custom code?
Search & evidence: Can you search by object attributes and export a fully packaged incident (video + logs + timeline)?
Interoperability: Native connectors for access control, LPR, intercom/PA, intrusion, RTLS?
Security posture: SSO/MFA, encryption, secure firmware lifecycle, audit logs, and privacy controls.
Lifecycle & TCO: Licensing model, GPU/edge options, cloud egress, and support for phased upgrades with legacy devices.
FAQs
What’s the fastest way to prove ROI?
Pick a use case with measurable waste (e.g., false alarms at the fence or loading dock dwell time). Benchmark a week of “before,” then show reductions in alarm load and average response time in the pilot zone.
Do we need new cameras?
Not always. You may keep existing cameras and add edge analytics you never turned on or upgraded or consider server-side pipelines. Prioritize upgrades where detection quality is truly limited by sensor capability (night scenes, long range, thermal needs).
Will AI replace operators?
No. It removes low-value monitoring and guides operators to better decisions. Humans still verify, intervene, and provide context.
How do we avoid “AI sprawl”?
Centralize around a VMS/PSIM that normalizes events and playbooks. Set governance: approved models, tuning rules, and change control.
Is this compliant with privacy expectations?
It can be. Design with privacy by default: role-restricted views, redaction on export for simple event review, clear retention schedules, and signage/notice where required.
The bottom line
AI-powered, agentic workflows are turning GSOCs from alarm factories into decision factories. The win isn’t just catching more events, it’s processing them with context, speed, and consistency, then proving it with data.
Ready to modernize your GSOC?
If you want a practical, vendor-agnostic plan—one that aligns policy, process, and technology—Theseus Professional Services can help. We specialize in assessments, design narratives, runbook development, and phased implementations that respect your existing investments while unlocking AI-driven performance.
Contact Theseus Professional Services to start a short, focused discovery: identify your top three use cases, map the integrations, and prove results in 30–60 days. Let’s design a solution that’s ideal for your system administrators and everyday users—scalable, secure, and measurably effective.
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Security professionals are constantly looking for innovative ways to secure their facility and provide a safe environment within their budget. And, they are also constantly looking for resources to help them achieve that mission while expert advice is hard to come by.
Fortunately, we have released a considerations guide that will help security professionals perform their own in-house security risk assessment.
What's Inside?
This guide is intended to assist you with performing an in-house physical security risk assessment. In many cases, assistance from a third-party expert, like Theseus Professional Services, is required.
Identification of missing or inadequate physical security measures that safeguard assets (people, property, and information) and critical business functions is of paramount importance. The findings of a security risk assessment are used to measure and communicate the level of risk to the organization.