How cities can adopt AI without sacrificing public trust

What does it mean to keep a city safe without making it feel watched?

For city governments in Asia deploying artificial intelligence (AI)-powered video security systems, the question sits at the heart of policy decisions.

Rising urban density, surging tourism numbers, and growing demand to do more with less have pushed AI-enabled video security to the top of the smart city agenda.

Yet, adoption remains uneven. But this is not because the technology isn't ready, but because public trust in how it is used often isn't.

Motorola Solutions APAC’s Vice President of Sales for Video Security and Access Control, Jason Tan, sees this tension play out in the region.

"Cities must strike a careful balance between implementing visible security measures and fostering a welcoming environment that doesn't deter residents with excessive restrictions nor discourage tourists,” he says.

That balance is exactly what governments are struggling to find.

The security overreach problem

When cameras proliferate without clear governance, residents reasonably ask: who is watching, what are they looking for, and where does the data go?

At the same time, governments in high-density cities and tourism-driven economies tackle the consequences of mismanaged security.

On one hand, too little security erodes public confidence and drives visitors away.

On the other hand, too much security invites political and regulatory backlash.

The answer, Tan notes, is not less technology, but more deliberate governance.

“Cities that are transparent about their use of security systems, implement privacy policies, conduct regular audits and manage data responsibly can help increase public trust,” he says, noting that cities can “clearly show that they're balancing security needs with their right to privacy.”

From reactive response to proactive governance

Tan notes that one of the significant shifts that modern AI security enables is from reactive incident response to proactive threat management.

According to him, the stakes of remaining reactive are high.

“A reactive incident approach is detrimental to city safety. Not only does it not prevent incidents from occurring, it keeps government agencies in the backseat, facing rising incident costs and inefficient processes, resulting in high crime rates that erode public trust.”

By analysing both live and historical data, AI-powered video analytics can identify anomalies in patterns, then flagging unusual crowd formations, loitering, or intrusion risks before situations escalate.

For civil servants managing public safety operations, this is a meaningful operational shift. This allows limited resources for law enforcement to be directed toward prevention than response.

“By prioritising prevention, agencies can improve community safety while lowering incident and emergency response costs,” he notes.

What good governance looks like in practice

For policymakers navigating an evolving regulatory landscape, the architecture of governance matters as much as the technology itself.

Tan points to several concrete measures that cities should embed into any AI security deployment.

Infrastructure design should include redundancy from the outset, including backup power, distributed storage and multiple network pathways, so that systems remain operational during critical situations.

A security platform that fails when it is needed most is not just an operational liability, but a trust liability.

Cities should also insist on open, vendor-agnostic platforms.

“Implementing vendor-agnostic standards and robust cybersecurity measures helps to prevent technology lock-in,” Tan notes, " and ensures cities have the flexibility to future-proof their systems to protect against threats.”

It is a strategic safeguard that retains a government's ability to adapt, audit, or switch providers as regulations evolve, and public expectations shift.

Finally, performance metrics and regular reviews should be built into the governance framework from day one, not added after deployment.

These reviews create a mechanism for accountability and provide the evidence base governments need to demonstrate responsible use to their publics.

Trust as a public service outcome

So, what does trust in AI security actually mean for city governments?

In cities where tourism is a significant economic driver, public confidence in safety directly shapes the decisions visitors make.

“When the public and tourists see security systems across the city, whether that is cameras or clear signage indicating video security in operation, they know that city agencies are protecting the area, leading to a greater sense of safety,” Tan says.

Residents, meanwhile, need to know that the tools their government deploys to keep them safe are not simultaneously observing their daily lives without oversight.

That dual commitment to both safety and civil liberties is what separates legitimate public security from security overreach.

For civil servants working at the intersection of technology policy and public safety, the question is not whether to deploy AI-enabled security tools, as the operational case is already compelling.

It is whether governments are willing to invest equally in the governance, transparency, and accountability frameworks that give those tools their legitimacy.

You can click here to watch a video case study or read a written case study for Pattaya City Hall.

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