In an era where generative AI reshapes how security operates, the imperative for digital trust has never been higher. This in-depth exploration draws on insights from Leo Levit, the chairman of ONVIF, to unpack how the security industry is adapting to AI-driven disruption, the pivotal role of international standards in preserving video authenticity, and why cross-stakeholder collaboration is essential as surveillance networks grow more complex and pervasive.
The evolving landscape: Generative AI’s impact on security and surveillance
Generative artificial intelligence is redefining the capabilities and expectations of modern security and surveillance systems. On the upside, AI augments traditional video analytics by enabling more precise behavior detection, improved crowd analysis, and enhanced object tracking. These advancements support proactive security measures, enabling more effective crime prevention and faster incident response. In addition, AI-driven analytics are enabling predictive surveillance workflows that anticipate potential threats before they fully materialize, allowing authorities and organizations to allocate resources more efficiently and respond with greater precision.
Yet this technological leap also introduces significant risks that must be managed with equal vigor. As AI-generated content becomes more accessible and convincing, there is a growing concern about manipulated footage, impersonated identities, and the spread of misinformation. The rapid evolution of synthetic media challenges the integrity of visual evidence and tests the strength of existing verification processes. In this context, the industry is increasingly focusing on establishing robust frameworks that can rapidly differentiate authentic footage from altered or fabricated material. The dual nature of generative AI—its potential to strengthen surveillance capabilities while amplifying new forms of manipulation—drives a comprehensive rethinking of trust mechanisms across the entire video lifecycle.
A dominant trend emerging from this shift is the heightened demand for video authentication and digital watermarking as foundational tools to verify the integrity of both footage and its production chain. These technologies are particularly critical for evidence management, where the provenance and authenticity of video can determine legal credibility and operational decisions. As organizations accumulate vast volumes of video data, there is a parallel push to ensure that authentication methods remain scalable, interoperable, and resilient across diverse systems and jurisdictions. This trend underscores a broader movement toward cryptographic verification and tamper-evident workflows that can withstand attempts to alter or retrofit footage after capture.
Another key trend is the intensification of collaboration among camera manufacturers, software developers, and standards bodies to create safeguards that operate across disparate systems and regions. The integration of AI into surveillance ecosystems is not confined to a single vendor or platform; it requires interoperability that preserves security, privacy, and trust as data flows between devices, apps, and analytics engines. This cross-sector cooperation is essential to avoid fragmented security practices that would undermine trust. In this environment, standards organizations are increasingly called upon to harmonize interfaces, data formats, and verification methodologies so that different components—across geographies and regulatory regimes—can work together seamlessly. Ultimately, AI is recalibrating not only how surveillance is conducted but how stakeholders perceive and measure trust in video authenticity, which remains a cornerstone of modern security.
Strengthening defenses against deepfakes: Security frameworks and real-world implementations
The rise of deepfake technology and other sophisticated manipulation techniques has prompted governments and organizations to elevate their security architectures with new verification and protection measures. A central pillar of this response is video verification, which encompasses a suite of techniques designed to confirm that a video file is authentic from the moment of capture through to its eventual viewing by authorities, courts, or private entities. This involves a combination of tamper detection, secure handling, and auditable trails that can be independently validated. By embedding verification checks at multiple stages of the video lifecycle, these frameworks aim to reduce the risk that compromised footage is admitted as credible evidence.
To augment verification, many jurisdictions are implementing camera tampering detection systems that can identify attempts to interfere with the capture process. These systems can trigger alerts when a camera is physically manipulated or when environmental factors degrade the integrity of the captured footage. In parallel, encryption is increasingly employed to protect video files throughout storage and transmission, ensuring that unauthorized actors cannot access unencrypted content or alter it without detection. The overarching aim is to establish a secure-by-design paradigm that preserves integrity from the instant footage is captured to the moment it is consumed by investigators or adjudicators.
A notable technical development in this space is the media signing approach being developed by ONVIF. Media signing provides a cryptographic proof that a video has not been altered since it left the camera sensor, thereby securing the footage at its source and enabling downstream verification regardless of which device or platform later processes the video. This source-level security is instrumental for maintaining trust across the entire chain of custody, from field capture to courtroom presentation. By anchoring authenticity at the origin, media signing helps ensure that later edits or substitutions do not compromise the integrity of the evidence.
In practice, forward-looking regions such as the United Arab Emirates and the broader Gulf Cooperation Council (GCC) are prioritizing smart city initiatives and AI-enabled surveillance as strategic objectives. In these contexts, deepfakes are viewed not only as a technical challenge but also as a national security concern that necessitates robust, scalable authenticity frameworks. Governments are progressively refining regulatory frameworks to demand authenticity, auditability, and transparency in surveillance systems. These shifts reflect a broader recognition that as data flows into increasingly intelligent networks, establishing verifiable credibility for the footage becomes as important as detecting actual threats.
A concrete example of this proactive approach is the Dubai Police’s move toward zone-based security, which illustrates how surveillance is becoming more sophisticated and proactive. This strategy demonstrates how authorities intend to leverage advanced analytics and secure data practices to anticipate and respond to incidents with greater precision. As data volumes grow and networks expand, the emphasis on authenticity does not merely enable better threat detection—it becomes a foundational requirement to ensure that the footage used in investigations and judicial processes is trustworthy. The combination of strong verification technologies and resilient governance structures is central to maintaining public confidence in surveillance systems as they become more reliant on AI-driven insights.
The role of international standards in digital trust and interoperability
International standards organizations such as ONVIF occupy a central role in safeguarding the integrity and interoperability of security systems amid rapid AI-driven change. ONVIF’s origins date back to 2008, when the organization established itself as a global forum for standardizing the interoperability of IP-based physical security products. Today, with a broad membership exceeding five hundred organizations worldwide and a catalog of more than twenty-five thousand conformant products, ONVIF has become a benchmark for trusted and secure video surveillance ecosystems. This expansive reach underscores the relevance of standardized interfaces and interoperable solutions in enabling diverse devices and software to function together reliably on a global scale.
As generative AI becomes more deeply embedded in video analytics and workflow automation, maintaining trust in the outputs of surveillance systems becomes increasingly vital. Standardized interfaces are not merely about device compatibility; they are about delivering secure, consistent data exchange across platforms. This consistency is essential for AI-driven systems that rely on data from multiple sources to detect events, infer patterns, and trigger automated responses. By defining common protocols and data models, ONVIF helps ensure that different manufacturers’ products can interoperate in a manner that preserves data integrity, privacy, and security across the entire ecosystem.
Beyond interoperability, ONVIF’s ongoing work on video authentication tools—such as media signing—serves a broader purpose: it enables law enforcement and legal professionals to verify whether video footage has been tampered with, regardless of which device manufacturer produced the source material. This capability transcends simple compatibility; it builds a foundation of trust across the legal and regulatory landscape. In an environment where evidence quality is under scrutiny and AI-assisted analysis is increasingly relied upon in decision-making, standardized authentication mechanisms help ensure that the inputs to those processes are credible and auditable. The overarching objective is to embed trust into every layer of the surveillance and security infrastructure, from sensors and networks to analytics platforms and investigative workflows.
In this context, international standards bodies are more than technical coordinators; they are partners in risk management and governance. They facilitate a collective approach to the challenges posed by AI-enabled surveillance by aligning technical capabilities with lawful and ethical considerations, regional regulatory requirements, and privacy protections. Standardization thus becomes a strategic instrument for resilience, enabling organizations to deploy advanced capabilities without compromising trust or accountability. As the AI landscape continues to evolve, ONVIF’s leadership in standardization provides a stable, forward-looking framework that can adapt to new threats, technologies, and use cases while preserving interoperability and trust across borders.
Innovations and best practices for restoring trust in video as credible evidence
Public skepticism toward digital evidence presents a significant challenge to the utilization of video in legal, corporate, and security decision-making. If AI manipulation undermines current legal precedents around video admissibility, courts may demand higher standards of validation or even exclude footage whose authenticity cannot be demonstrably proven. Restoring confidence in video evidence, therefore, hinges on two foundational pillars: transparency and traceability. These pillars guide the development and deployment of technologies, processes, and governance structures that collectively reinforce the credibility of video assets.
Transparency involves making verification processes visible and auditable to relevant stakeholders, including investigators, judges, and the public. This means clearly documenting how footage is captured, processed, stored, transmitted, and analyzed, as well as how authentication assertions are generated and validated. Techniques such as transparent auditing trails, disclosure of metadata schemas, and accessible verification reports contribute to an environment where the provenance of video evidence can be independently assessed. Electrooptical and cryptographic assurances should be combined to provide a comprehensive picture of the footage’s origin and integrity.
Traceability complements transparency by ensuring that every step in the video’s lifecycle can be traced and reconstructed. Innovations such as encrypted digital signatures and embedded forensic watermarking are gaining traction across both public and private sectors. Encrypted signatures provide a tamper-evident record that verifies the exact time and place of capture, the camera identity, and the chain of custody for the footage. Forensic watermarking adds another layer of protection by embedding imperceptible markers that survive typical processing and distribution workflows, enabling later verification of authenticity even if the video undergoes transformations. These technologies together create a robust framework for detecting unauthorized alterations and confirming the legitimacy of the material as evidence.
In practical terms, implementing these innovations requires a multi-layered approach. Organizations should adopt secure-by-design practices that prioritize integrity from the outset of video capture, rather than retrofitting protections after data has already been generated. This includes securing camera hardware, protecting communication channels, and enforcing tamper-resistant storage and access controls. It also means establishing auditable governance around how video data is used, shared, and analyzed, with clear policies that address privacy concerns and compliance requirements. Standards-based authentication mechanisms, such as media signing, should be integrated into procurement and deployment roadmaps to ensure end-to-end verifiability across devices and ecosystems.
Education and awareness are equally important. Stakeholders across legal, regulatory, and procurement domains must understand the capabilities and limitations of AI-enhanced surveillance, including the potential for misinterpretation of AI outputs. Clear standards for truthfulness in automated detections, explainability in AI-driven decisions, and explicit articulation of confidence levels can help reduce misinterpretations in courtrooms and boardrooms alike. The goal is not to stifle innovation but to establish credible frameworks that enable responsible use of video analytics while safeguarding the integrity of evidence and public trust.
Finally, collaboration remains a cornerstone of effective trust-building. The convergence of hardware manufacturers, software developers, standards bodies, law enforcement, and policymakers is essential to craft cohesive, interoperable, and enforceable solutions. Through ongoing dialogue, shared best practices, and joint roadmapping, the industry can align technical capabilities with legal and societal expectations, ensuring that video evidence maintains its value as a source of truth even as AI technologies evolve. In this integrated approach, ONVIF’s leadership in standardization and media authentication plays a pivotal role, guiding the adoption of robust protections that support credible, accountable security operations and judicial processes.
Conclusion
As generative AI reshapes surveillance and security, the imperative to preserve trust in video evidence becomes more urgent and complex. The dialogue between industry leaders, standards bodies, and policymakers highlights a path forward that emphasizes interoperability, authenticating technologies, and transparent governance. With organizations like ONVIF driving interoperable standards and pioneering methods such as media signing, the industry is building resilient foundations for credible video in an AI-enabled world. The convergence of secure-by-design practices, advanced authentication, and cross-sector collaboration will determine how effectively digital video remains a trustworthy asset for security, justice, and public safety in the years ahead.