AI-powered video analytics are already a trusted tool for security providers – they help teams monitor more effectively, respond faster and reduce missed incidents, reports DeepAlert.
The next evolution in this space comes from Large Language Models (LLMs) – systems like ChatGPT that can understand and generate human language and when combined with video analytics, can open new possibilities for operational efficiency and incident response.
AI in security today
Most monitoring centres already use AI to handle some of the heavy lifting, such as detecting motion in restricted zones, recognising licence plates or flagging unusual activity.
These tools free operators to focus on higher-priority tasks rather than scanning hours of footage.
For example: A perimeter breach can trigger an alert within seconds; loitering detection can automatically notify operators of potential threats; access control logs can be cross-checked with CCTV feeds to confirm whether a person entering is authorised.
What LLMs bring to the table
LLMs build on these capabilities by processing and responding to natural language requests.
In practice, that means operators could search video archives with a simple query like, “show me all incidents near the south gate last night.”
Moreover, they could receive AI-generated incident reports, complete with timestamps and descriptions, ready for review and also get summaries of multiple events at the end of a shift without spending extra hours on documentation.
Because LLMs are trained on vast amounts of text data, they can also handle context.
For example, if a site has multiple “north gates” in different locations, the AI can use incident history and access control logs to determine which one you meant.
Vision-Language Models
A branch of LLMs – VLMs (Vision-Language Models) – can interpret images and videos alongside text. This makes them particularly suited for CCTV monitoring and investigation. Examples include:
- Identifying whether a person in a still image is wearing a high-vis vest or security uniform
- Describing what’s happening in a short clip: “Two people are waiting near the fire exit after hours”
- Detecting unusual patterns, even for events where there isn’t much historical footage to train on
Practical applications
Some of the most immediate use cases for LLMs in security monitoring include:
- Smart video search – quickly locate footage that matches a natural language description
- Automated reporting – reduce the time spent writing incident reports without compromising detail
- Alert verification – use AI to cross-check data from multiple systems before escalating an incident
- System integration – link CCTV, access and alarm data for a more complete operational picture
Looking ahead
As these technologies mature, we can expect more control room tools that act as an “AI co-pilot”, assisting with verification, documentation and even suggesting response actions in real-time.
Multilingual reporting, site-specific knowledge and deeper integrations are also on the horizon.
LLMs aren’t replacing existing AI video analytics, they’re enhancing them.
For security providers, this means faster access to information, reduced workload for operators and more accurate, timely responses for clients.
