Generative artificial intelligence is rapidly transforming the security and surveillance landscape, presenting both powerful opportunities and significant risks. As AI-generated content becomes more capable, video analytics gain new dimensions of precision and speed, enabling operators to detect behaviors, track objects, and predict suspicious activity with greater efficiency. Yet these same capabilities open avenues for manipulation, deception, and the creation of sophisticated misinformation. This duality is intensifying the need for robust digital trust frameworks that can quickly discern authentic footage from altered material, while ensuring that the benefits of AI-driven insights are realized without compromising security, due process, or civil liberties. Against this backdrop, industry bodies like ONVIF—the Open Network Video Interface Forum—are stepping forward to shape global standards for interoperability and security in IP-based physical security products. Since its inception in 2008, ONVIF has established itself as a cornerstone of interoperability, bringing together manufacturers, software providers, and end users to ensure that devices from different vendors can work together in a secure, predictable manner. With more than 500 members worldwide and over 25,000 conformant products, ONVIF has set benchmarks for trusted video surveillance systems, providing a foundation that is increasingly vital as the world confronts AI-driven manipulation and the growing complexity of surveillance networks. This evolving role underscores a broader industry imperative: to embed trust into every layer of the surveillance ecosystem, from capture to analysis to presentation in legal, corporate, or public settings.
The rise of generative AI and its reshaping of global security and surveillance
The infusion of generative AI into security and surveillance marks a watershed moment for the industry, reframing both capabilities and expectations. On the one hand, AI-driven technologies unlock advanced video analytics that were previously unattainable at scale. Behavior detection, crowd dynamics analysis, and precise object tracking can now be implemented with higher fidelity, enabling operators to respond faster to emerging threats and to allocate resources more efficiently. Predictive surveillance, leveraging patterns learned from vast datasets, holds the promise of proactive prevention, potentially reducing crime or mitigating risk before it materializes. These benefits extend to a range of environments, from critical infrastructure and transportation hubs to large campuses and city-wide deployments where smart sensing and autonomous decision-making can improve public safety and operational efficiency.
On the other hand, the rapid growth of AI-generated content introduces new vulnerabilities that demand careful risk management. Manipulated video, forged identities, and misinformation can erode the reliability of footage as evidence or as a decision-support signal. The security industry must confront the fact that AI is not only a tool for detecting threats but also a catalyst for sophisticated deception. This reality pushes for a comprehensive approach to authenticity that is integrated throughout the surveillance lifecycle—from capture, storage, and transmission to processing, analysis, and presentation. The challenge is how to balance the transformative value of generative AI with the imperative to preserve the integrity of visual data. In this context, the industry is moving toward frameworks that can reliably differentiate authentic footage from manipulated content, while maintaining the efficiency and scalability that AI enables.
A notable trend arising from this shift is the heightened emphasis on video authentication as a core requirement. Organizations are increasingly recognizing that knowing the provenance of footage—the origin, the chain of custody, and the integrity of the production process—is essential for trust. Digital watermarking and other forms of tamper-evidence are becoming more commonplace, not merely as cosmetic safeguards but as integral components of evidence management. These technologies aim to provide verifiable signals about how and when a video was produced, and whether it has undergone any alteration since capture. The emphasis on authenticity is not simply a technical concern; it is a governance and policy priority that affects how footage is used in law enforcement, judicial processes, corporate investigations, and security operations. The result is a broader shift toward building trust at the data layer, the device layer, and the network layer alike.
To translate these trends into practical, scalable outcomes, the industry is strengthening collaboration across the entire ecosystem. This includes camera manufacturers, software developers, integrators, and standards bodies working in concert to design safeguards that function across different systems and regions. The complexity of AI-driven workflows—where data may move through multiple analytics stages, across various vendors’ platforms, and into decision-support tools—requires interoperability that goes beyond traditional compatibility. It demands standardized interfaces and shared security models that ensure consistent behavior, predictable performance, and verifiable integrity irrespective of where the footage was captured or processed. In short, AI’s influence on surveillance is accelerating the demand for standardized, cross-vendor solutions that can uphold trust even as AI capabilities multiply and become more ubiquitous.
Protecting against digital manipulation: deepfakes and security frameworks
The increasing accessibility of deepfake technology has elevated the importance of robust security frameworks designed to protect video evidence and prevent fraud. Governments and organizations are responding by integrating rigorous video verification methods into security protocols and legal processes. A central pillar of this response is the deployment of tamper detection mechanisms that can identify signs of alteration, as well as encryption and secure-by-design principles that protect footage from the moment of capture through to its viewing, whether by law enforcement, the judiciary, or private sector stakeholders. The overarching aim is to ensure that video remains trustworthy and admissible as evidence across diverse use cases.
Within ONVIF’s broader strategy, a concrete and technically grounded approach to authenticity is the development of a method called media signing. This approach is designed to prove that a video has not been altered since it left the camera sensor—the point of capture. By securing the footage at its source, media signing provides a foundational assurance of integrity, enabling downstream verification that is independent of the device manufacturer. The significance of securing footage from the outset cannot be overstated: once footage has been altered, restoring trust becomes a far more complex and costly endeavor. Source-level protection helps preserve a verifiable chain of custody, which is essential for credible investigations and for the courts to assess the authenticity of the material presented as evidence.
Regional perspectives further illuminate the urgency of this issue. In forward-looking regions such as the United Arab Emirates and the broader Gulf Cooperation Council (GCC), smart cities and AI-enabled surveillance are strategic priorities that intersect with national security considerations. In these contexts, deepfakes are increasingly treated as a national security issue in need of robust, scalable safeguards. Governments are progressively updating frameworks to require authenticity, auditability, and transparency across surveillance systems. The aim is to create safeguards and accountability mechanisms that can withstand the rapid growth of data and the expanding reach of automated analysis. This includes initiatives such as zone-based security models that reflect a more proactive and technologically sophisticated approach to public safety. These efforts illustrate how public sector leadership is shaping security norms and expectations for the entire ecosystem, driving demand for interoperable, standards-based protections that cross borders and vendors.
As surveillance networks expand and data volumes rise, the importance of ensuring the reality of captured footage becomes as critical as the real-time detection of threats. The balance to strike is one of resilience and practicality: building systems that can quickly identify manipulated content while maintaining operational efficiency and protecting civil liberties. Achieving this balance requires not only advanced technical solutions but also governance frameworks that define acceptable use, data handling practices, and accountability. In practice, that means combining tamper-detection capabilities, secure-by-design architectures, and transparent policies that explain how footage is captured, processed, and stored. It also means designing for resilience so that if manipulation is detected, there are clear pathways for verification, audit trails, and recourse. The end goal is to increase the trustworthiness of video across all stages of the data lifecycle, ensuring that footage can be relied upon by authorities, organizations, and the public.
The central role of international standards in digital trust and interoperability
International standards bodies play a pivotal role in maintaining digital trust and ensuring interoperability in an era of rapid AI evolution. ONVIF was established as a global and open industry forum with a clear mandate: standardize the communication between IP-based physical security products so that devices and software from different manufacturers can interoperate seamlessly. This mission has proven to be transformative, making ONVIF the de facto standard for interoperability in surveillance and creating a broad ecosystem in which diverse components can be integrated into cohesive security solutions. The international and open nature of ONVIF’s work means that governments, businesses, and institutions around the world can deploy security systems with a predictable level of compatibility, a critical factor for large-scale implementations that involve multiple vendors and complex supply chains.
As generative AI becomes more embedded in video and analytics workflows, maintaining trust in the output of surveillance systems becomes increasingly important. ONVIF’s standardized interfaces help ensure secure and consistent data exchange across platforms, a foundational requirement for building robust AI-enabled security architectures. These interfaces support not only basic interoperability but also the reliability and integrity needed for AI-driven decision-making. In practice, this means that data consumed by analytics modules, whether for real-time alerting or for post-event investigations, can be trusted to reflect the original footage accurately and without undisclosed alterations. The standardization effort thus serves a dual purpose: it enables seamless operation across heterogeneous environments and it reinforces the trust that stakeholders place in the surveillance system as a whole.
A particularly critical area of ONVIF’s work concerns video authentication tools, including media signing. By standardizing the methods used to establish and verify the authenticity of video footage, ONVIF can facilitate verifications that are independent of device manufacturers. This is essential for law enforcement, judicial processes, and forensic analysis, where the ability to validate that footage has not been tampered with is foundational to the credibility of the evidence. The emphasis here extends beyond mere compatibility—they are about embedding trust into every layer of the surveillance and security infrastructure. The standardization of authentication mechanisms helps ensure that even as techniques for deception evolve, there are interoperable, auditable means to verify the integrity of the data.
In this light, ONVIF’s role encompasses more than technical compatibility; it is about cultivating a trusted ecosystem where data can be shared and analyzed with confidence across borders and organizations. The practical implications are significant: standardized data exchange, consistent security rules, and interoperable security controls create a more resilient surveillance network capable of supporting AI-driven analytics while maintaining rigorous integrity standards. For policymakers and practitioners, this means clearer governance, more predictable procurement paths, and a shared framework for evaluating and upgrading security systems as new threats and technologies emerge. The result is a more reliable, scalable, and auditable infrastructure that can serve a wide range of use cases—from public safety to enterprise security—without sacrificing the principles of transparency and accountability.
Innovations and best practices to restore trust in video as evidence
Public skepticism about digital evidence is a real and growing concern. If AI manipulation undermines confidence in video admissibility and reliability in legal, corporate, and security contexts, courts and organizations may face difficult decisions about what constitutes valid evidence. The consequence could be a reevaluation of admissibility standards, with potential exclusions for footage whose authenticity cannot be demonstrated beyond reasonable doubt. Restoring trust in video evidence, therefore, hinges on two foundational pillars: transparency and traceability. These pillars guide the development of practical tools and processes that make it feasible to verify the integrity of video from capture to courtroom or boardroom.
Transparency involves making the provenance and handling of video data clear and auditable. Innovative approaches such as encrypted digital signatures and embedded forensic watermarking are gaining traction because they provide independent signals about the capture event and subsequent handling. Encrypted signatures help verify that the data originated from the claimed source and has not been altered in transit, while forensic watermarking adds a covert, verifiable marker that can survive certain transformations and processing steps. Taken together, these tools enable stakeholders to determine when, where, and how a video was captured, and to assess whether any tampering has occurred. The practical value of such technologies lies in their ability to create auditable trails, making it possible to reconstruct a reliable sequence of events even when the footage has undergone complex processing or transmission.
Additionally, robust authenticity frameworks require governance and operational controls. This includes clear policies on data retention, access controls, cryptographic key management, and disciplined change management for systems that handle video data. It also means implementing end-to-end security measures that cover the capture device, the network transport, the storage environment, and the analytics pipeline. When organizations publish transparent policies about how footage is captured, protected, and analyzed, they send a strong signal about their commitment to integrity. This is complemented by technical measures such as integrity checks, tamper-evident logging, and periodic independent audits to validate that the systems function as intended.
Beyond technology and governance, the legal and regulatory environment plays a critical role in shaping trust. Courts and regulatory bodies increasingly demand demonstrable evidence of authenticity, along with clear documentation showing the chain of custody and the processing steps that footage has undergone. The combination of technical safeguards and legal clarity creates a more predictable framework for using video in decision-making. It also incentivizes organizations to invest in higher security standards and to adopt interoperable, verifiable solutions that can withstand scrutiny in diverse settings. As the industry continues to evolve, best practices will include not only state-of-the-art cryptographic techniques and watermarking but also standardized verification workflows, certified training for personnel, and ongoing collaboration with legal professionals to ensure that evidentiary standards align with technological capabilities.
Organizations should also consider integrating standardized verification signals into their incident response and investigations. When a security event is detected, teams can rely on a verified lineage of the video data—from the camera sensor to the analyst workstation—to support timely and accurate conclusions. This, in turn, can enhance investigative efficiency, reduce the risk of misinterpretation, and improve outcomes in both civil and criminal contexts. In practice, the goal is to create a credible, reproducible, and defensible evidentiary trail that can endure the scrutiny of legal processes and public accountability. By coupling technical authenticity with transparent governance and legally aligned practices, the industry can strengthen the reliability of video as evidence and restore public trust in digital footage.
To operationalize these principles, stakeholders are pursuing a multi-layered approach that combines device-level protections, secure data pathways, and verifiable analytics. This includes enforcing strict authentication and authorization for accessing video streams, using tamper-resistant storage solutions, and applying cryptographic protections that persist through processing and distribution. It also involves developing and adhering to shared methodologies for evaluating the integrity of video data, so assessments are consistent across jurisdictions and use cases. Engaging with law enforcement, judges, compliance officers, and civil society groups helps ensure that the adopted practices meet real-world needs and expectations, while remaining adaptable to new threats and technological advances. In sum, restoring trust in video evidence requires a holistic, end-to-end strategy that aligns technical safeguards with governance, legal standards, and stakeholder collaboration.
Regional adoption, governance, and the public-security imperative
The regional context matters enormously when it comes to deploying AI-enabled surveillance and establishing trusted video workflows. In the UAE, the GCC, and cities pursuing smart-city initiatives, there is a heightened awareness of the strategic value of authentic, auditable video data. Governments are prioritizing frameworks that demand authenticity, auditability, and transparency across surveillance ecosystems. These regional strategies often involve coordinated efforts across multiple agencies, standards bodies, and private-sector partners to ensure that the security architecture remains coherent and enforceable in practice. The emphasis on national security considerations means that solutions are evaluated not only on technical performance but also on governance, risk management, and accountability. The Dubai Police example illustrates how zone-based security concepts are pushing surveillance to be more proactive and technologically sophisticated, blending real-time analytics with robust governance to deliver outcomes that are both effective and accountable.
Beyond the public sector, regional governance also shapes the way vendors and operators approach interoperability. When standards bodies like ONVIF define common interfaces and authentication mechanisms, regional players can adopt a unified baseline while allowing room for local adaptations. This harmonization is essential for cross-border investigations, extradition processes, and international collaborations in public safety. At the same time, regional policies may impose additional requirements around privacy, data residency, and human-rights considerations, all of which influence how video data is collected, stored, and used. Organizations operating in multiple regions must design architectures that respect diverse legal regimes while preserving a consistent standard for integrity and trust.
The broader governance landscape must also address the supply chain dimensions of security technology. As AI-enabled surveillance depends on a chain of devices, software, and services sourced from different vendors, supply chain integrity becomes a key risk factor. Standards-based procurement practices, risk assessments, and vendor assurance programs help mitigate these risks by providing a transparent framework for evaluating the security posture of each component. In practice, buyers can look for evidence of conformity to established standards, independent security testing, and verifiable update and patching processes. This approach supports a resilient ecosystem where AI-driven surveillance can function reliably without exposing users to unchecked vulnerabilities.
The future of collaboration, interoperability, and industry evolution
Looking ahead, the security industry is likely to see deeper, more systematic collaboration among stakeholders across sectors and geographies. Interoperability will extend beyond the basic ability to exchange data and will encompass consistent security models, unified authentication practices, and shared governance over how video data is captured, analyzed, and used. Industry forums, standards bodies, regulatory agencies, and end users will increasingly align on best practices that balance innovation with accountability. In this environment, ONVIF’s role as a global, open platform for standardization becomes even more critical, serving as a backbone for a trusted, scalable, and transparent surveillance ecosystem that can adapt to AI-driven requirements.
As generative AI continues to influence all aspects of video analytics, the need for standardized, auditable, and interoperable solutions will intensify. The industry may see the emergence of cross-vendor verification services, more robust cryptographic frameworks, and new forms of verifiable metadata that accompany video streams throughout their lifecycle. These developments will support not only security operations but also regulatory compliance, judicial review, and public confidence in the use of AI-enabled surveillance technologies. The ongoing collaboration among manufacturers, standards bodies, and public institutions will be essential to maintaining a balance between innovation and trust, ensuring that the deployments of today and tomorrow remain secure, accountable, and effective.
The practical implication for practitioners is clear: invest in interoperable, standards-based solutions that prioritize authenticity and integrity from the outset. This includes adopting media signing and other authentication tools, embracing end-to-end security architectures, and participating in broader industry conversations about governance, transparency, and accountability. Organizations should also anticipate evolving regulatory expectations and prepare to demonstrate verifiable evidence of authenticity, provenance, and handling throughout the life cycle of video data. By aligning with international standards and fostering cross-sector collaboration, the security community can harness the benefits of AI while mitigating risks, ultimately delivering safer, more trustworthy surveillance solutions for public safety, corporate security, and the communities they serve.
Conclusion
In an era defined by rapid advancements in generative AI and the growing complexity of surveillance networks, digital trust stands at the center of how security systems are deployed, evaluated, and relied upon. The interview with Leo Levit, chair of ONVIF, underscores a shared recognition across the industry that interoperability, authenticity, and collaboration are essential to unlocking the full potential of AI-driven surveillance while safeguarding the integrity of evidence and the rights of individuals. ONVIF’s ongoing work in standardizing interfaces, advancing video authentication tools like media signing, and promoting secure-by-design principles reflects a deliberate strategy to build trust into every layer of the security infrastructure. The organization’s global footprint, with hundreds of members and thousands of conformant products, highlights the scale and urgency of creating interoperable, trustworthy systems that can operate across regions, vendors, and use cases.
As governments, businesses, and law enforcement agencies continue to rely on video data for decision-making, the industry’s focus will remain on ensuring that footage can be trusted from capture to court. The emphasis on video authentication, digital watermarking, encrypted signatures, and forensic techniques represents a practical, multi-faceted approach to tackling deepfakes and manipulation. Regional examples from the UAE, GCC, and Dubai Police illustrate the real-world application of these principles, showing how authentic, auditable surveillance is not only technically feasible but also strategically essential for national security and public safety. The path forward involves sustained cross-sector collaboration, ongoing refinement of standards, and a commitment to transparency and accountability that can withstand the evolving challenges posed by AI.
Ultimately, the trajectory of AI-enabled surveillance will be shaped by the industry’s ability to integrate advanced analytics with rigorous governance, robust security controls, and verifiable authenticity mechanisms. By embedding media signing, encrypted protections, and standardized verification into the core of surveillance architectures, stakeholders can deliver systems that are not only powerful and efficient but also credible and trustworthy. The future of digital trust in video surveillance hinges on a holistic approach that combines technology, policy, and practice, ensuring that AI remains a force for enhanced security and responsible governance rather than a source of doubt and vulnerability.