AI-Powered Surveillance: The Future of Video Monitoring

As artificial intelligence agents become increasingly adept, video surveillance systems are undergoing a dramatic transformation, ushering in an era of intelligent automation, contextual decision-making, and enhanced support for human operators. Forget the static, pre-set rules of the past.

Instead, these systems are evolving into responsive entities, dynamically assessing and reacting to real-time information. This shift redefines the role of human operators and significantly boosts the overall efficiency of security operations.

The key question driving this revolution? Autonomy. Specifically, how much decision-making power can – and should – be entrusted to machines. According to Florian Matusek, Director of AI Strategy and Managing Director of Genetec Vienna, this is the defining question for the future of AI in video surveillance.

“AI agents can now automate more tasks, responding dynamically to situations without relying on pre-defined rules,” Matusek explains. “Instead of asking how much human intervention is necessary, we should be asking how much *should* be required.”

This subtle but crucial change in perspective reflects a broader trend: the industry’s move from reactive systems to proactive, predictive capabilities. Today’s video management systems (VMS) must function in complex, mission-critical environments – think airports, power plants, and city-wide surveillance networks – where even small errors or delays can have serious consequences.

“VMS systems are deployed in critical locations where wrong decisions can have a big impact,” Matusek cautions. “That’s why humans should always be kept in the loop for critical decision-making, ensuring final judgments are made with human oversight. AI systems should augment human abilities, not replace them.”

From Analytics to Autonomy

At Milestone Systems, this transformation is viewed through the lens of what Chief Technology Officer Rahul Yadav calls “Action Quotient,” or AQ. This metric gauges how intelligently and autonomously a system can respond to stimuli – much like autonomous vehicles interpreting and reacting to ever-changing road conditions.

“This shift represents what we call Action Quotient, or AQ, which is the power to act intelligently and autonomously, similar to how Tesla’s self-driving cars don’t just process road conditions but navigate complex traffic scenarios in real time,” Yadav explains.

In the world of video security, AQ translates to AI agents that can detect anomalies, identify security threats, coordinate responses, and even predict future incidents based on patterns and historical data. According to Yadav, these systems are constantly learning and improving with each incident they process.

“AI agents can handle routine monitoring, identify threats, coordinate responses, and predict incidents,” he says. “Their value comes from learning from each incident and improving over time, creating increasingly effective security operations.”

Despite these advancements, both experts agree on one fundamental principle: human oversight remains essential. No matter how sophisticated, technology cannot completely replace human judgment, especially in unpredictable or ethically sensitive situations.

“The most effective security operations combine technology and human expertise,” Yadav emphasizes. “Human operators excel at understanding context, making nuanced judgments, and handling unexpected situations. The key is finding the right balance where technology handles predictable scenarios while humans focus on situations requiring judgment and empathy.”

Privacy and Bias: Ethical Frontiers of AI

As AI systems gain greater autonomy, they inevitably encounter ethical and regulatory challenges, particularly concerning data privacy, algorithmic fairness, and responsible usage. In video surveillance, where systems are constantly capturing and processing sensitive personal information, the stakes are particularly high.

Matusek warns against blindly trusting data without rigorous vetting and user consent. “AI systems are only as good as the data they have been fed,” he says. “Biased data sets lead to biased decisions and should be avoided.”

He stresses that organizations developing AI-based video analytics must be diligent about data quality and transparency. “Data within data sets needs to be vetted, and customer data should only be used after their explicit consent. This is why, whenever developing AI systems, responsible AI guidelines should be followed.”

Yadav echoes these concerns, framing them as both a moral obligation and a strategic advantage. “Responsible technology development has become a crucial competitive advantage,” he asserts. “Organizations must prioritize ethical frameworks that protect privacy while enabling innovation and build trust with users who select security partners based on their ethical track record.”

To put this into practice, Milestone is developing robust governance structures that dictate how data – especially video data – is collected, stored, processed, and used in training machine learning models.

“Privacy considerations are paramount in video security where sensitive information is constantly captured,” Yadav notes. “VMS companies must develop clear governance frameworks for data usage, especially when training AI models. We’re exploring how to leverage video data ethically, creating systems trained on responsibly sourced data.”

The issue of bias remains one of the most persistent – and potentially dangerous – challenges in AI development. Biased training data can lead to systems that unfairly target or overlook certain demographic groups, introducing risks of both over-policing and under-detection.

“Bias presents another critical challenge,” Yadav says. “AI systems learn from their training data, and any biases will be reflected in the resulting systems. Great AI requires not just abundant data but ethically sourced, diverse data that covers the full spectrum of scenarios without unfairly favoring certain groups or situations.”

Implications for Integrators and End Users

For systems integrators, consultants, and end-users, the evolution of AI in video surveillance presents both new opportunities and new responsibilities. On one hand, AI-powered automation promises to improve operational efficiency, reduce false alarms, and enable faster response times.

On the other, it requires a deeper understanding of how these systems work – and the ethical standards they should be held to.

This transition also necessitates a shift in training. Security personnel must be educated not just on how to use AI tools, but on how to supervise them. Integrators need to know how to assess AI offerings for bias, privacy compliance, and operational transparency.

Moreover, buyers are becoming more discerning. Organizations increasingly seek partners who offer not only technical performance but also a clear commitment to responsible AI development. As Yadav puts it, “Users select security partners based on their ethical track record.”

Looking Ahead

Both Genetec and Milestone agree that the path forward lies in partnership – between human intelligence and machine learning, between innovation and governance. AI will not replace the human element in video security; instead, it will redefine and elevate it.

“AI systems should augment the users’ abilities, not replace them,” says Matusek. This sentiment may well define the next generation of video surveillance: smart, ethical, and above all, human-centric.

As AI agents continue to mature, the challenge for the security industry will be to embrace this potential responsibly, ensuring that smarter surveillance doesn’t come at the cost of trust, fairness, or accountability.