AI Revolutionizes Physical Security: Navigating Innovation, Intelligence, and Governance in a Transforming Landscape

The physical security industry is undergoing a dramatic transformation, driven by the convergence of artificial intelligence, cloud computing, and the Internet of Things. This potent combination is forging intelligent, interconnected networks that promise to redefine how we protect our spaces and assets. According to Omdia’s 2025 Video Surveillance & Analytics Report, the global video surveillance market, a $25.9 billion industry in 2024, is projected to surge to $37.1 billion by 2029, boasting a 7.5% CAGR fueled by the widespread adoption of AI integration and intelligent analytics. This evolution signifies more than just technological advancement; it represents a fundamental paradigm shift from traditional, reactive monitoring to proactive, intelligent security operations.

Four interconnected forces are reshaping the security landscape: intelligent AI systems, the proliferation of edge computing, evolving governance frameworks, and the ever-present cybersecurity imperatives. Each element builds upon the others, creating a comprehensive transformation that demands strategic coordination and a forward-thinking approach.

1. Generative and Agentic AI: The Intelligence Revolution

The integration of generative AI and agentic AI systems is fundamentally reshaping video surveillance operations, enabling a move away from reactive responses to proactive, informed decision-making. Modern AI-powered systems now leverage vision-language models (VLMs) trained on vast datasets of image/video-text pairs. This approach fosters sophisticated understanding capabilities that far surpass traditional computer vision methods. These large models, now integral to video surveillance systems, offer several key advantages:

Enhanced contextual understanding: AI systems can now discern between suspicious loitering and casual waiting, providing nuanced scene interpretation that dramatically reduces false alarms while simultaneously improving threat detection accuracy.

Accelerated deployment cycles: Foundation models enable rapid fine-tuning for scenario-specific applications, allowing organizations to swiftly adapt security systems to emerging threats and evolving operational requirements.

Real-time intelligence: Modern systems possess the ability to monitor passenger and vehicle flows in urban transport networks, detect rule violations automatically, and identify suspicious patterns through intelligent analytics. This transforms security from a passive recording function to an active threat prevention system.

2. Edge AI Proliferation: Distributed Intelligence Architecture

As these intelligent systems become more sophisticated and capable, they demand substantial computational resources that traditional, centralized architectures simply cannot support efficiently. The computational demands of advanced AI have catalyzed a fundamental shift toward edge processing. Omdia’s research indicates that a staggering 61% of all camera shipments are expected to be AI cameras by 2029.

This proliferation is fueled by advancements in computer vision models and compression technologies, including knowledge distillation, which allows large AI models to operate efficiently on resource-constrained edge devices. With the integration of multiple sensors and diverse data sources, modern AI-enabled video cameras can create comprehensive situational awareness. For example, in industrial applications, surveillance cameras are now integrating with specialized sensors for gas leak detection and fire prevention.

Edge AI deployment transforms security networks from centralized monitoring systems into intelligent, autonomous networks capable of independent decision-making and coordinated response, reducing latency and improving overall system resilience.

3. Global AI Governance: Regulatory Framework Evolution

As AI-powered security systems become more pervasive and autonomous, their potential impact on society grows exponentially. This expanding influence has prompted governments worldwide to develop comprehensive regulatory frameworks designed to ensure responsible AI deployment while fostering continued innovation.

The passage of the EU’s AI Act in August 2024 represents a watershed moment in AI governance, establishing comprehensive regulatory frameworks that are poised to influence global security industry practices. This landmark legislation creates clear boundaries for acceptable AI applications while promoting responsible innovation, directly addressing concerns raised by the proliferation of intelligent surveillance systems.

Notably, the AI Act explicitly prohibits the untargeted scraping of facial images from CCTV footage, emotion recognition in workplaces and schools, and predictive policing based solely on profiling—setting global precedents for ethical AI deployment.

Compliance imperatives: Organizations must navigate evolving regulatory requirements while maintaining operational effectiveness. The regulatory frameworks provide structured guidance for ethical AI implementation while addressing cross-border data compliance challenges, creating both constraints and opportunities for innovation. These governance requirements become particularly complex when applied to distributed edge AI systems that process sensitive data across multiple jurisdictions.

4. Cybersecurity and Privacy: The Security Paradox

While regulatory frameworks establish the boundaries for responsible AI deployment, they also highlight a critical challenge: the expanded attack surfaces and cybersecurity risks created by intelligent, interconnected security systems. The very technologies that enable proactive threat detection also create new vulnerabilities that must be systematically addressed. AI-enabled video surveillance system backends host massive amounts of private and confidential metadata, making them attractive targets for cybercriminals while subjecting organizations to significant regulatory penalties. GDPR violations, for instance, can result in fines of up to 4% of annual global turnover or €20 million.

With growing concerns over cyber-physical security, new legislation is expected to commence and come into force in the next few years, such as the EU’s Cyber Resilience Act (CRA), which requires “secure by design” and “secure by default” implementations. Navigating the evolving regulatory and compliance landscape is becoming critical to the security industry.

Conclusion: Orchestrating Transformation

The convergence of AI intelligence, edge proliferation, regulatory governance, and cybersecurity imperatives is creating an interconnected ecosystem that promises unprecedented security capabilities while introducing complex management challenges. Success in this transformed landscape requires organizations to embrace a holistic orchestration approach that integrates:

1. Advanced AI capabilities that enable proactive threat detection while respecting regulatory boundaries.
2. Distributed edge intelligence that processes data locally while maintaining cybersecurity resilience.
3. Comprehensive regulatory compliance frameworks that enable ethical AI deployment across jurisdictions.
4. Robust cybersecurity measures that protect AI models, edge devices, and sensitive data simultaneously.

Success hinges on managing these four transformative forces as an integrated whole rather than as separate initiatives. Organizations that successfully orchestrate this complex transformation—seamlessly integrating intelligent AI systems with secure edge architectures while maintaining rigorous regulatory compliance and cybersecurity standards—will define the future of intelligent physical security and capture the significant growth opportunities in this rapidly evolving market.

Tommy Zhu is Principal Analyst, Physical Security, at OMDIA