Watermarking, for instance, is a way to detect piracy after the fact. In the context of AI and ML, however, watermarking is extremely helpful because it helps create data sets that are used to train algorithms. For example, if there are 2,000 data points collected from one specific user’s device, and a video service provider finds that content that was specifically streamed to that user’s device is now on the dark web, those data points can be used by AI and ML to help determine what were the contributing factors to the piracy taking place. Watermarking, therefore, is crucial for training algorithms that are then also used to anticipate piracy. Video service providers can choose to watermark a vast amount of content, or only use watermarking when there is significant concern that nefarious activities could take place – it can be a balancing act due to the desire to manage costs and security.

Video service providers’ Digital Rights Management (DRM) is only protecting content. To illustrate its roll, people often use the example of wooden dolls where there’s one small doll encapsulated by a doll that’s slightly larger, and that doll is then encapsulated by yet another doll that is slightly bigger, etc. In Multi-DRM, the smallest doll is the content, and the outer layers could be the player that’s playing back the content, the application that the player is embedded into, and the device (such as a phone) that app is installed on. The industry has progressed from just securing the actual content to now protecting the player from being hacked. That’s done by preventing the ability to extract encryption keys that would otherwise enable the content to be shared elsewhere. With the communication between the player and the content secured, there’s now a huge roadblock for pirates who are salivating over an easy target. Additionally, there can be added benefits to Multi-DRM. For example, if a video content provider requires users to input credit card information and personal identifiable information (PII) Multi-DRM is now protecting that data as well. It’s this layering of powerful security that enables AI and ML to capture data to be far more preemptive in the fight against content theft and stolen revenues.

The vibe in the industry is also changing from one of intense complexities surrounding integrations and studio-mandated security to one of greater flexibility, cost-effectiveness and proactiveness. Providers used to feel they had to spend immense amounts of engineering resources, but with the advent of a zero-code approach to security, there is a way to inject protection technologies into the application with no need for a provider to write any new code. With less overhead surrounding the ongoing use of security in applications that are continuously updated and enhanced, providers are embracing the improved levels of security and harnessing the power of the cloud to make sure things such as code obfuscation is automated and streamlined. The concept of zero code has been successful across numerous other industries ranging from business intelligence to web design, and it is now newly introduced and highly welcomed into the media and entertainment space so that video service providers don’t necessarily have to be security experts to gain powerful content protections. That translates into notable cost savings and less dependence on niche, expensive engineering talent. To put that in perspective, it may take several months or longer to roll out an entirely new video content platform, but only a few hours or a day of that is now related to the implementation of anti-piracy and application security.

The reason anti-piracy remains so important amid the introduction of so many new streaming services is simple – stolen content means that the streaming service is losing subscribers they would otherwise gain or retain, and the original content owner is losing money because its licensed content is now available for free, and potentially significant revenue is clearly being lost. When content is available for free, it ultimately diminishes the value of that content. Why would the next organization come and ask to license that content for a premium if that same content is already available for free. The easier and faster that anti-piracy protections are to implement, the more it serves as a business enabler for all stakeholders. Taking it a step further, the AI and ML now being used within security offerings makes that process even smarter – arming video service providers with a vast array of intelligence surrounding the use of their systems as well as how and what to do when piracy is sniffed out or anticipated.

The theme for content protection in the 2020s has definitely changed from only blocking bad guys to COLLECTING data that is helpful throughout the delivery process as well as protection efforts. Artificial intelligence and machine learning have opened up doors to new levels of intelligence that were simply not available to video service providers just a few short years ago – including the ability to determine what countermeasures are most appropriate in certain circumstances. Accompany that with the convenience of the cloud and cloud-native apps and platforms, and the result is a more heavily secured industry armed with insights that position a provider to be in control of their business as well as their ongoing revenues.

By blending anti-piracy efforts with data plus powerful AI and ML-powered cybersecurity technology, the media and entertainment industry can outsmart pirates by preventing them from exploiting the delivery avenues of today and tomorrow.