Continuous evaluation instead of one-time approval

24 december 2025

In the previous installments of this series, we explored step-by-step where AI in video surveillance adds genuine value, when autonomy can be justified, and why professional expertise is the deciding factor. This naturally brings us to a final, yet crucial question: how do you ensure that AI remains manageable once it has become part of daily operations?

Once AI is deployed structurally and, in some cases, permitted to act autonomously, technology alone no longer suffices. At that stage, governance becomes essential—not as a layer of bureaucracy, but as a prerequisite for safeguarding trust, continuity, and responsibility.

Why governance is becoming indispensable

Without clear governance, AI shifts from a tool into a “black box.” Decisions are made, alerts are generated, and actions are performed without clarity on who is responsible for what. This is not a theoretical risk. In practice, we see organizations struggling with questions such as: Why did this happen? Who authorized this? How do we prevent a recurrence?

Governance is the answer to those questions. It is not about stifling innovation, but about organizing oversight, ownership, and control. Especially in video surveillance, where decisions have a direct impact on people, processes, and sometimes public spaces, governance is not a luxury—it is a necessity.

Ownership: Who is responsible for AI?

One of the first governance questions is surprisingly simple, yet often goes unanswered: Who owns the AI? Is it an IT system, a security tool, or an operational aid? In many organizations, AI falls between departments. IT manages the infrastructure, Security utilizes the output, and Compliance observes from the sidelines.

Without explicit ownership, fragmentation occurs. Decisions regarding configuration, updates, or adjustments are made on an ad hoc basis. Effective governance requires a clear division of roles, where one designated owner maintains the overview and weighs competing interests.

Transparency and explainability as a foundation

Oversight of AI begins with insight. Organizations must be able to explain why a system does what it does. This is necessary not only for auditors or regulators but also internally for operators and management. When an AI system prioritizes an alert or acts autonomously, it must be clear which signals triggered that action.

This does not mean every user needs to understand the underlying model, but it does mean that decisions must be traceable. Consequently, explainability is not just a technical attribute; it is an organizational requirement.

Human oversight remains mandatory

Part 3 of this series made it clear that humans remain ultimately responsible. Governance translates this principle into practice. This means there must always be mechanisms in place to intervene, course-correct, or temporarily scale back autonomy.

In video surveillance, we see that high-functioning organizations utilize clear escalation levels. AI is permitted to flag events and make suggestions, but high-impact decisions are always confirmed by human personnel. This human oversight is not intended to distrust AI, but rather to maintain trust in the system’s output.

Continuous evaluation instead of one-time approval

A common mistake is viewing AI as “finished” once implementation is complete. Governance demands the exact opposite: AI must be evaluated periodically. Behavior changes, environments evolve, and what works well today may be less reliable tomorrow.

This requires structural evaluations of performance, error margins, and the impact on processes. This should not be an incident-driven check, but a standard component of maintenance and management. Only then can autonomy remain responsible.

Governance in the context of European regulations

Within Europe, AI governance is not an optional choice. Legislation such as GDPR, NIS2, and the AI Act explicitly states that organizations remain responsible for decisions prepared or executed by systems. Transparency, data minimization, human control, and auditability are not abstract concepts, but concrete requirements.

For video surveillance, this means that governance is inextricably linked to compliance. On the contrary: well-established governance makes compliance easier and prevents situations where AI must be restricted or rolled back after the fact.

The role of partners and advisory

We observe that many organizations cannot—or do not wish to—answer these governance questions alone. This is understandable. AI in video surveillance simultaneously touches upon technology, processes, people, and legislation. Therefore, there is a growing need for partners who provide more than just technology, but also help structure oversight and responsibilities.

At IDIS, we view this role not as an “extra service,” but as a logical component of a mature relationship. We do this not by dictating governance, but by working together to see what fits the organization, the risks, and the maturity level of the deployment.

From technology to trust

If there is one common thread in this series, it is this: AI only becomes valuable when it is embedded in professional expertise. Governance and oversight are not the opposites of innovation; they are the conditions for it. Without control, there is no trust. Without trust, there is no acceptance.

AI in video surveillance therefore requires more than clever algorithms. It requires clear choices, defined responsibilities, and the willingness to view technology as part of a larger whole.

Conclusion

AI in video surveillance is neither a hype nor a miracle cure. In Part 1, we saw that value is created through proper application. In Part 2, it became clear what mature AI truly entails. In Part 3, we explored when autonomy is responsible. In this fourth part, it is evident that governance and oversight are the binding factors that hold everything together.

The future of video surveillance with AI will not be determined by what is technologically possible, but by the care with which we manage it. Organizations that understand this build not only safer systems but also more sustainable trust.