Control rooms have always been the beating heart of security operations but the world around them has changed.
In the past, operators could reasonably monitor what was happening on a handful of screens.
Today? There are thousands of CCTV feeds and the footage is constant.
Alerts are never-ending and the pressure/expectation is still the same: spot every potential threat, and make the right call as fast as possible.
But in 2025, that model just doesn’t hold. Not because of any particular failure, but because the scale has grown beyond what human teams alone can manage.
This is where AI assistants come in. Not as replacements for operators, but as partners. As an extra set of eyes – and context – in the room.
What are the key challenges of the modern CCTV control room?
Arguably the most obvious challenges faced by security teams and those in the control room is the sheet volume of surveillance video far exceeding what human teams can feasibly process.
Operators are expected to detect relevant activity across hundreds or even thousands of feeds, often in real time.
Ask any control room operator and they’ll tell you: the pressure doesn’t come from doing the job, it comes from trying to do it all at once.
Dozens of alerts come in every hour, many of them are false and most of them are irrelevant.
The operator is forced to triage in real time. Is someone loitering, or just waiting for a ride? Is that smoke, or just a shadow caught in the light?
And all of this happens while the legal and safety stakes remain high – especially in places like airports, transport hubs, or public venues.
It’s a constant balancing act: act too soon, and risk a false alarm. Act too late, and risk something worse. What operators need isn’t more alerts – just better, more reliable ones.
How does AI help CCTV monitoring?
Traditional CCTV systems rely heavily on human judgment. Operators sit in front of screens, scanning for anything that looks unusual – but with hundreds of feeds and limited context, things inevitably get missed.
AI changes that by taking on the heavy lifting. Instead of treating every movement as a potential threat, AI systems look at the bigger picture.
They assess patterns, behaviours, and context over time and, instead of just detecting that something moved, they understand what moved, why it matters, and whether it’s worth an alert at all.
For example, an AI assistant might track how a particular area behaves hour by hour, day by day.
If a sudden spike in activity happens at an odd hour, or someone enters a restricted zone during downtime, it flags it – not just because motion was detected, but because it breaks the normal pattern.
This kind of intelligent filtering is what helps reduce alert fatigue and improve operator focus. It’s not about watching more, it’s about understanding more.
What is an AI Assistant?
By using advanced artificial intelligence models, these AI assistants are essentially a new tracking system that can understand both visual data and natural language, offering a new layer of operational clarity.
This is made possible by the emergence of Vision-Language Models – AI systems trained to interpret and describe visual scenes in response to text-based queries.
Rather than relying on pre-coded rules, these models learn to identify and describe contextually relevant behaviours, objects, and scenarios.
For example, an operator might question:
- Is there a weapon in this scene?
- Is someone lying down in an unusual place?
The AI assistant however interprets the video feed and provides a reasoned response, supporting quicker and more confident decisions.
AI assistants aren’t here to replace human operators; they just enhance their capability.
The key benefits of AI assistants in CCTV operations
1. Real-time clarification
When alerts are ambiguous, AI assistants can provide immediate explanations of what is occurring in a scene. This eliminates the guesswork and reduces the risk of misinterpreting a critical incident.
2. Contextual alerting
AI assistants consider the context such as the time of day and behavioral mannerisms and use this to determine whether something is genuinely unusual or simply part of regular activity.
3. Reduced alert fatigue
By acting as a filter between raw alerts and operator screens, the AI assistant ensures that only high-confidence, context-rich alerts reach human attention. This reduces cognitive load and increases operational focus.
4. Natural language interaction
Operators interact with the system using plain language, allowing for a collaborative dynamic between human and machine. This is especially valuable in high-stress scenarios where rapid understanding is critical.
5. Continuous learning
These AI models learn from the environment over time. As they ingest more data, their accuracy in identifying unusual or significant events increases, enhancing their value as a real-time partner.
IntelexVision’s Aurora: AI Assistant for Real-Time Video Understanding
At the forefront of this evolution is Aurora, IntelexVision’s AI assistant integrated into the Sentry platform. Aurora is powered by a proprietary Vision-Language Model that interprets visual alerts and supports operator decisions through conversational interaction.
When operators encounter unclear or borderline alerts, Aurora allows them to pose questions about the scene and receive clear, contextual answers.
It adds an intelligent verification layer that mitigates ambiguity, ensuring that critical threats are neither dismissed nor misjudged.
More than just a response engine, Aurora analyses visual elements across multiple parameters (object type, behaviour, location, time-based patterns) and references them against expected norms.
It can flag the presence of weapons, verify PPE compliance, or even identify suspicious crowd formations.
Improving and enhancing the operator’s role through better understanding of AI
By embedding AI directly into control room workflows, IntelexVision is redefining how operators engage with surveillance systems.
Instead of passively monitoring screens, operators become decision-makers supported by intelligent technology.
In live deployments, the combination of Sentry and Aurora has:
- Enabled up to 15x increase in operator efficiency
- Reduced false positives by up to 95%
- Allowed a single operator to manage 300-800 cameras effectively
These results are not just performance metrics; they are enablers of safer, more responsive security operations across transport hubs, critical infrastructure, and commercial environments.
As AI continues to evolve, its role in surveillance will not be about replacement but collaboration.
The operators of the future will not be overwhelmed by data; they will be empowered by insight.
With AI assistants like Aurora, control rooms transition from reactive environments to strategic, intelligence-led hubs of decision-making.
The success of any surveillance operation depends not just on the number of cameras or the volume of data, but on the ability to interpret and act on that data.
The control room of the future is not just smarter – it is more human, more informed, and decisively more capable thanks to the support of new technologies.
