ISJ hears exclusively from Jeff DiDomenico, VP of Strategic Development at Trackforce about why organizations must move beyond AI hype to build structured, accountable AI use cases within security operations.
What separates real AI value in security operations from the hype?
The biggest difference between real value and hype comes down to whether AI is actually improving operational outcomes.
A lot of the early conversation around AI in security focused on what the technology could theoretically detect or automate.
But in practice, security teams don’t operate in a vacuum.
They’re managing incidents, coordinating staff and making decisions in real time.
If AI isn’t helping those processes run better, faster or more accurately, it’s not delivering real value.
Where we see AI making a meaningful impact is helping security teams process large volumes of information and prioritize what actually matters.
Cameras, sensors, access systems and alarms generate enormous amounts of data.
AI can help filter through that noise and surface potential risks much faster than a human operator could alone, but the key is that those insights have to feed into a structured response process.
Part of making that work is bringing the security officer along in the process. As long time security industry leader Eddie Sorrells has said, the industry is shifting from “observe and report” to “observe and respond.”
That shift doesn’t happen automatically with new technology.
It requires training and ongoing training, so officers understand how to interpret AI-driven insights and take appropriate action in the field.
If an AI system flags something unusual, but that alert just sits in a dashboard or generates constant noise, it doesn’t help anyone.
The real value comes when AI improves situational awareness and supports decision-making within an operational workflow and when the people on the ground are equipped to act on it.
That’s where it becomes a force multiplier for security teams rather than just another layer of technology.
How can organizations reduce false positives from AI surveillance alerts?
False positives are one of the biggest concerns organizations face when they start using AI for monitoring or surveillance.
No AI model is perfect and in complex environments like transportation hubs, campuses or large facilities, there are countless variables that can trigger alerts.
The first step is making sure that AI is deployed for clearly defined use cases.
If you try to use a single model to detect everything everywhere, you’re going to create unnecessary noise.
Successful deployments start with specific operational problems, things like detecting unusual crowd movement, identifying restricted-area access, or flagging safety risks.
Narrowing the scope helps the technology perform more accurately.
Another important factor is continuous tuning and evaluation. AI systems improve when organizations regularly review alert outcomes, analyze where mistakes occur and adjust thresholds or models accordingly.
That process requires visibility and data transparency so teams can understand why alerts were triggered.
Finally, integrating AI alerts into a structured incident management process helps filter out noise.
When alerts are routed through a workflow that includes verification, escalation procedures and human review, teams can quickly determine whether something is legitimate or not.
Over time, that feedback loop helps the system become more reliable and reduces unnecessary disruptions.
What role should human oversight play in AI-driven security monitoring?
Human oversight is probably the most essential piece of the puzzle.
AI must augment security teams, not replace them.
Security environments are complex and the vast majority of situations require context, judgment and situational awareness that machines simply don’t have.
AI is very good at identifying patterns or anomalies across massive amounts of data. But determining whether something is truly a threat requires understanding the broader environment.
A person can evaluate context like whether a situation is normal for that location, whether there’s a known event taking place, or whether the alert could be triggered by harmless activity.
That’s why many organizations are adopting a “human-in-the-loop” approach.
AI surfaces potential risks and prioritizes them, but trained security personnel review those alerts before actions are taken.
This approach helps reduce the risk of errors and ensures decisions remain accountable.
Human oversight is also critical from an ethical and governance standpoint. Security decisions often have a direct effect on employees, customers and the public.
Organizations need clear processes to review AI outputs, document decisions and maintain transparency around how technology is being used.
When AI operates within a structured framework that includes human judgment, it becomes a powerful support tool rather than just a completely automated decision-maker.
Why is it important to connect AI insights directly to workforce and incident response workflows?
One of the most common mistakes organizations make is treating AI as a standalone tool.
They deploy analytics or monitoring software, but the insights it generates aren’t actually connected to the teams responsible for responding to incidents.
Security operations are fundamentally about coordination. When something happens, whether it’s a safety issue or unusual behavior, someone has to investigate, communicate with other teams and take action.
If AI alerts aren’t connected to those operational workflows, they create more information but not necessarily better outcomes.
When AI insights are fed directly into workforce and incident management systems, that’s where they become actionable.
This is when we can see an alert automatically trigger a response process, notify the appropriate personnel or assign tasks to security staff in the field.
That level of integration ensures the right people have the right information at the right time.
It also improves accountability and reporting.
When AI alerts and response actions are documented within the same operational system, organizations can review how incidents were handled, identify trends and refine procedures over time.
That visibility is critical for improving both security performance and operational efficiency.
How can organizations deploy AI surveillance while maintaining public trust and privacy?
There’s no mistaking it; trust is one of the most important factors in how organizations deploy AI, especially in security environments where surveillance technologies are involved.
People want to know that these systems are being used responsibly and that their privacy is being respected.
A strong starting point is adopting privacy-by-design principles.
That means limiting data collection to what is necessary for the specific security objective and implementing safeguards such as role-based access controls, data minimization and clear retention policies.
Not everyone should have access to sensitive information and data shouldn’t be stored longer than needed.
Transparency is also important.
Organizations should clearly communicate how AI systems are being used, what types of data are being analyzed and what safeguards are in place.
When stakeholders understand the purpose of the technology and the protections around it, it helps build confidence.
Equally important is accountability.
Security teams should maintain audit trails that show how alerts were generated, how decisions were made and what actions were taken.
That level of traceability allows organizations to evaluate system performance, identify potential bias and demonstrate compliance with regulations or internal policies.
Another important factor is visibility across the entire security operation.
As organizations adopt more AI tools, fragmented systems can create gaps in oversight.
Bringing alerts, data and workflows into a unified view, a “single pane of glass”, helps teams maintain context, apply consistent governance and better understand how AI is performing in real-world operations.
At the same time, organizations need to consider the rise of deepfakes and synthetic media.
Organizations need to recognize that AI-generated content can be used to create convincing false visuals or audio, which can complicate surveillance and incident response.
Just as important as deploying AI tools is educating teams and clients on their limitations, AI is not a panacea.
Training staff to question, verify and validate AI-driven insights is critical to avoiding overreliance and maintaining sound judgment.
Ultimately, maintaining public trust comes down to balancing innovation with responsibility.
AI has enormous potential to improve safety and situational awareness, but it must be deployed within a framework that prioritizes transparency, human oversight and respect for privacy.
When organizations approach it that way, they can harness the benefits of the technology while maintaining the trust of the people they serve.
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