Trust the Source: Why Authenticating Video Data Is Essential

We’ve lived it. We’ve experienced it firsthand.

Artificial intelligence (AI) has had a big impact on the video surveillance and security sectors over the past year — automating tasks such as threat detection, object recognition and predictive analytics with better accuracy than ever. These advancements have been essential tools to help empower security teams. With AI’s help, personnel can augment existing skills and be more focused and intelligent while improving overall efficiency. Now, with the integration of technologies like generative AI into video surveillance and security platforms, we are experiencing the creation of new use cases for the systems we’ve invested in; however, this progress comes with challenges, particularly regarding the authenticity of video data.

The rise of AI-generated content, such as deepfakes, has introduced significant risks to the integrity of digital media, including video and security data. Altered or fabricated videos increasingly undermine public trust, threaten businesses and complicate legal proceedings. According to a 2024 YouGov survey, nearly 81% of respondents doubted online content’s trustworthiness. This sentiment underscores the dire need for solutions that can ensure the authenticity of video content…and all digital media.

But security is distinctively critical. Video data serves as the backbone for informed decision-making, risk assessment, and mitigation strategies; however, it faces unique vulnerabilities. Emerging technologies like AI and quantum computing pose significant challenges to the industry’s ability to safeguard this data. Current protective measures, such as encryption and watermarking, are increasingly being undermined.

To address these risks, adopting AI-resistant and quantum-resistant technologies is becoming imperative. These advancements will set a new standard for protecting digital content, ensuring the integrity of video data. Equally crucial is the establishment of an independent chain of custody to meet evolving evidentiary requirements. As the demand for transparency in security systems grows, resistant technologies will drive the industry forward, leapfrogging decades of progress.

When the authenticity of video data is called into question, it undermines trust in the very systems designed to protect us. Security leaders must ask themselves: how can they rely on their video surveillance investments if they cannot ensure the validity of the footage?

Consider this real-world example: Following Hurricane Helene’s impact on North Carolina, AI-generated images of a crying child holding a puppy in a flooded street went viral. Many believed these images were authentic, leading to unwarranted criticism of emergency responders’ efforts in the impacted region. Such incidents highlight the potential for AI to create widespread confusion and erode trust in digital content. And this is just one example of the many problems we’ve seen arise with AI-generated content this year alone.

The U.S. Department of Homeland Security is also on alert. It has been reported that video manipulation tools have advanced significantly over the past year, making it increasingly difficult to distinguish authentic footage from altered versions. This growing uncertainty presents a critical challenge for the security industry, which relies on various forms of digital content daily. Without a reliable method to authenticate video data, the credibility of these systems and the information they produce is at risk.

For years, the security industry has benefited from AI’s ability to automate processes and enhance insights. Video analytics have enabled applications like line crossing, object recognition and motion detection, yet while many organizations have embraced AI, few have implemented measures to protect themselves from AI-generated threats. Quite frankly, the time is now for us to take action.

I can’t stress this enough: Ensuring the safety and authenticity of video content has never been more critical. Security teams must develop a comprehensive understanding of their video data —including its origin, creation and chain of custody — to differentiate between genuine content and manipulated versions. By maintaining a verifiable record of a video’s source and timeline, security leaders can confirm its authenticity and protect their organizations from potential risks.

The implications of AI-driven video manipulation extend far beyond security applications and even individual organizations. They affect public trust, legal systems, media and the broader global community. As an industry, our job is to help build a transparent ecosystem where the integrity of our digital content is beyond question. The stakes are too high to ignore.

The views and opinions expressed in guest posts and/or profiles are those of the authors or sources and do not necessarily reflect the official policy or position of the Security Industry Association.