Spotlight: Agentic AI in Monitoring Centers
Inside today's monitoring centers, Agentic AI is quietly becoming part of the team. It verifies alerts, contacts the right people, generates reports, and documents every action as it happens.
People make the judgment calls. Agentic AI executes the mechanical steps that used to create bottlenecks. This isn't automation in the abstract. It's real-time assistance built directly into the workflow.
In this model the AI doesn't just observe, it works. It places outbound notifications, handles follow-up calls, and keeps records straight while the operator stays engaged with the situation. The result is faster response with far less manual effort.
From Overload to Partnership
Every monitoring shift begins with screens, alerts, and constant triage. Most alerts are not real threats. They're environmental triggers, false positives, or events that don't require escalation. Each one still demands verification, acknowledgment, and documentation, and that workload erodes operator focus and reaction time.
Agentic AI changes that equation. It filters alerts before they reach the operator's console, validating events across video, analytics, and site data. The operator sees only verified detections and spends time on what actually needs human judgment.
The process shifts from reactive review to a working partnership between human and AI. Operators no longer watch hundreds of feeds hoping to catch something. They manage verified incidents that arrive packaged with context and ready for action.
Division of Labor Rewritten
In a traditional GSOC, operators work sequentially. Check the feed, verify the event, call the contact list, wait for callbacks, type the report after the fact.
Agentic AI rewrites that workflow. It takes on the parts of the job that demand precision, repetition, and exact timing. A single verified detection can now trigger multiple actions in parallel.
When an incident is confirmed, the AI can place three calls to stakeholders at the same time, something no human operator can do, while the operator watches the live feed. It documents each call, logs the outcome, and prompts the operator on what comes next.
Follow-ups that once depended on memory now happen automatically. Reports that once took hours appear in real time, already populated with event data, timestamps, and operator notes.
The division of labor plays to each side's strength. Operators bring reasoning and context. Agentic AI brings speed and consistency. Together they close the loop on every incident, from first alert to final record.
Collaboration in Real Time
A recent live shift in a remote monitoring center shows what this looks like in practice.
An after-hours human detection comes in from an employee parking lot. The operator pulls the alert, verifies the person on the live camera, and classifies the event as a trespasser. One click on the response panel hands the outbound notifications to the AI.
Within seconds, the system is calling the right people. Each contact hears a clear spoken update:
"A person was detected in the employee parking lot. The operator reviewed the live cameras and initiated the response panel. Do you have any questions?"
The site supervisor asks about a backpack. The AI answers immediately, referencing the live detection. He asks whether the person is still on site. The AI confirms with verified information from the scene.
While that conversation unfolds, the operator stays on the cameras, confident that every contact is being informed and every action is being logged.
By the time the situation resolves, the report already exists. Video, audio, and a transcript of the conversation, with no post-incident paperwork. No follow-up calls slipped. No manual reporting. No missed steps.
That's the collaboration in motion. The operator leads, the AI executes, and the system documents itself.
Transparency Builds Trust
Human and AI collaboration only works long term if everyone can see what the AI did.
Every action Agentic AI takes is initiated by the operator. The console shows verification logic, communication logs, and escalation outcomes in real time. That visibility builds confidence with operators and clients alike.
Supervisors can review exactly how an event was handled. Who was contacted, what was said, how long each call lasted, and what follow-up occurred. Instead of scattered notes and delayed reports, the full incident history lives in one traceable record.
The clarity goes beyond compliance. Every handled event becomes training material. Teams can study what worked, spot what didn't, and refine protocols based on evidence rather than anecdote.
In an industry where accountability is everything, transparency can't be optional. Agentic AI makes it automatic.
Real-World Example: SARA Assist
To see this in action, watch the demonstration below.
View the SARA Assist Demonstration Video
The most advanced version of this collaboration is already running in select GSOCs and monitoring centers. It's called SARA Assist.
Built on the SARA Agentic AI framework, SARA Assist works inside RADSOC's incident management environment. It handles outbound communication, reporting, and follow-ups as part of live operations.
It fits into the operator's existing workflow as a constant partner, scaling the team's capacity without adding headcount.
SARA Assist is a preview of where monitoring is headed. People and AI working side by side, each doing what they do best. Humans bring insight and context. Agentic AI brings speed, reach, and consistency.
Closing Thought
Agentic AI is augmenting the operator, not replacing the control room.
Every monitoring professional gains an assistant that never misses an alert, never forgets a follow-up, and never stops documenting.
That frees operators to focus on judgment, leadership, and outcomes while the system handles the rest.
It's how the next generation of GSOCs will run. Humans leading, AI executing, and incidents resolved faster than either could manage alone.
David Marsh
Vice President of Marketing
RAD Security
