If you work in security operations, most days feel like Bill Murray in “Groundhog Day”. A sea of camera feeds, endless alerts, and a clock that never stops ticking. The problem, often times is not a lack of data; it’s responding to what actually matters at the right time.
At EPIC iO, we’ve been building enhanced capabilities into our solutions that will help bring clarity to chaos: Generative AI Summarization with Event Consolidation. Being able to quickly and efficiently summarize what’s happening across multiple cameras and sensors into a digestable snapshot (or consolidated event) is paramount to adapting to the quantitative growth of security events. Not because operators need more technology, but because the deluge of events need perspective.
That’s the role we believe GenAI can play in security: helping people move faster from events to decisions.
What GenAI should — and shouldn’t — do
We don’t believe AI should make unproctored enforcement decisions. Accountability should always stay with people.
GenAI can be an enabler to provide decisioning support, not replace it. Properly leveraged, GenAI can help operators see what’s happening, understand it faster, and act with more confidence — but the decision to intervene always belongs to a human.
AI can:
- Surface and consolidate information
- Summarize activity
- Identify Patterns
- Suggest a course of action based on Standard Operating Procedures
But it shouldn’t decide:
- Who to confront
- When to escalate
- How to respond
We integrate GenAI into our solutions as a support tool, not an automated authority.
How we use GenAI in real operations
In real-world security operations, reviewing video is tedious and time consuming. Operators scrub through footage, jump between cameras, cross reference timestamps, and reconstruct what happened while that event is still transpiring and other events are starting..
GenAI can help refine other AI capabilities, such as Computer Vision, and other technologies, such as data entity relationship mapping, that turns raw video and sensor data into a concise, readable context. Instead of “Let’s pull up all the video we have,” it becomes “Here’s what happened.”
We use GenAI across several parts of the workflow:
- Event summarization – turning video and sensor data into short descriptions an operator can scan in seconds
- Triage and prioritization – highlighting what matters first so attention goes to the highest risk events
- Operator assist – providing relevant context (locations, timelines, related alerts) so humans can make faster decisions
- Report writing support – speeding up post incident documentation with draft summaries that humans review and finalize
- Alerts for quick action – surfacing meaningful changes in real time so operators don’t miss emerging issues
That shift alone can save minutes to hours per event — sometimes more depending on the scale and timelapse of the event. In security, time is everything.
Clearing up misconceptions about GenAI
There’s a persistent misconception that Generative AI is either a magic solution or an uncontrollable risk. When properly trained, deployed, and managed, it’s neither.
Most operators assume that GenAI is sentient or has been trained using random public data. So, questions about “full autonomous mode AI” (i.e. Skynet) or statements like “it is always wrong” (i.e. Barney Fife decisioning and response) always bubble to the surface.
As we discuss in more detail about having the right solution-oriented architect and ecosystem, you will discover that properly deployed GenAI doesn’t replace real human security expertise; it amplifies it.
Why system design matters more than the model
Here’s a contrarian truth: the model isn’t the hard part. Design and leverage are usually the larger challenge points.
Where you place cameras, what the cameras can actually capture, which areas are high risk, and how alerts flow to operators all matter more than chasing the latest model benchmark.
GenAI can’t fix:
- Blind spots and poor angles
- Bad lighting and blurry images.
- Incomplete coverage areas
If the base inputs are flawed, even the most highly trained and orchestrated AI won’t help.
That’s why we work with our customers within their existing systems — making targeted improvements rather than starting from scratch.
Simple changes like thoughtful camera placement, coverage based on risk instead of convenience, and workflows designed together can dramatically improve results.
The best outcomes come from partnership: combining customers’ knowledge of their environment with our engineering expertise.
AI works best when it’s part of a well-designed system, not dropped in as a silver bullet.
How we think about AI architecture
We don’t think in terms of standalone “models.” We think in terms of systems.
There are many layers of AI but AI is also only one layer among many others: cameras, sensors, networks, user interfaces, and audit trails all contribute to whether an operator can act quickly and confidently. A useful analogy is GPS: it suggests routes and recalculates when things change, but you still drive.
Good architecture:
- Provides context, not just raw alerts
- Shows confidence levels so operators can calibrate trust
- Allows human override at every critical step
- Records decisions for auditability and training
That’s how trust is built — operationally, not philosophically.
What we’ve learned so far
After deploying GenAI, correctly, in real security operations, a few patterns stand out.
Event Summaries with batching and composite views dramatically reduce review time, particularly during complex or multicamera incidents. Operators respond faster when they get context, not just notifications. Fatigue goes down when noise is filtered and they can focus on the events that genuinely matter.‑camera incidents. Operators respond faster when they get context, not just notifications. Fatigue goes down when noise is
Just as importantly, trust increases when AI responses are detailed in the system so that operators can see why something was flagged, what sources contributed, and how confident the system’s response was, and if there is a pattern.
AI does not solve the problem. Leveraging AI correctly within a response system will assist humans in solving the problem.
How we design and deploy AI responsibly
Guardrails matter.
For EPIC iO, responsible design means:
- Clear boundaries on what AI does and does not do
- Audit trails for both AI outputs and human decisions
- Human review loops for critical actions
- No black‑box decisions that can’t be explained
- Transparent limitations, communicated up front
We’re intentional about what we don’t claim. AI can be powerful, but it’s still a tool. In security, that distinction isn’t academic — it’s operational.
Generative AI has enormous potential in security operations — but the real power isn’t in making “the decision” or replacing people.
Properly leveraged, GenAI can give clearer information, faster insights, and reduce noise so that people can act with confidence.
When humans stay in control, AI becomes what it should be:
A Force Multiplier.
Michael Knight, CTO
EPIC iO