WASHINGTON — Imagine sitting in the control room at Acme Teleport. It’s Friday night, and you’re braced for the inevitable: A surge of high-priority data traffic from financial institutions closing out their end-of-week transactions. Suddenly, you get an alert. Teleport-13 left its high-power amplifier chain on again, spraying your satellite with interfering noise.
In the past, you’d carefully watch your spectrum analyzer and monitor automated systems that adjust frequencies, reroute signals and program redundant pathways to avoid data loss, while you tried to contact Teleport-13 to turn off its systems. But tonight, AI’s got your back. It’s been trained on past incursions and can now predict disruptions, dynamically selecting alternative frequencies and preemptively adjusting settings, without even breaking a sweat.
Predictive interference mitigation is just one of many areas where artificial intelligence is expected to be a game changer for satellite ground systems. Ultimately, some experts believe we have only scratched the surface of potential AI uses. As more teleport and satellite network operators begin deploying AI solutions, they are uncovering new possibilities, from hybrid network management to the development of proprietary AI models and APIs.
In the Beginning, There Was Data
Satellite ground systems are essentially monitoring stations that house troves of data, from TT&C and latency metrics to hardware performance, spectrum usage, maintenance logs, weather data and more.
“In today’s communications world, having 10 or 15 different paths is not uncommon,” said Frank Czulo, President of Advanced Networks at Network Innovations. “When you can pull data in from teleports and put that into a large model, you now have almost a visual representation of space.”
As ground service providers tackle the complexities of AI, there’s an awareness that having data and processing data to create value are very different. Like anything in business, it’s critical to understand what the organization is trying to accomplish with AI before implementation. Will Mudge is the Vice President of Engineering and Operations at Speedcast, a global independent teleport operator that recently released its AI-enabled SIGMA platform. He explained one of the first steps in AI implementation is capturing relevant data and putting it in a database. That can be challenging when pulling data from “disparate systems,” like multi-vendor antenna control units, amplifiers, upconverters, downconverters, etc.
“The next step is cleaning that data up in a way that machines can understand it,” he continued. Not only does the data have to be clean and sourced, but any irrelevant, bad or faulty data can pollute an entire model. This is where the principle of “garbage in, garbage out” is critical.
From that point, you can apply machine learning (ML), where a computer identifies patterns in data and builds up to AI algorithms that can begin to problem-solve and make probabilistic decisions based on the data. Generative AI is where the information learned from applying ML is used to generate new data that resembles the data it was trained on.
Intelligent Networks
Perhaps one of the more exciting use cases for AI in satellite ground systems mirrors its use by terrestrial telcos. Managing the complexity of a satellite network has been a major hurdle in bridging terrestrial and non-terrestrial communications networks. Despite a slow start, the satellite community is gradually accepting software-defined networking (SDN). AI functionalities are fundamentally software-based. Because anything software can incorporate AI, the shift to SDN could mark a major step forward for AI-enabled hybrid networks.
“We’re following very closely what telecom operators and mobile network operators are already doing because they are ahead in implementing this,” said José Sánchez Ruiz, Chief Customer Operations Officer at Hispasat. “We’re trying to see from their experience how we can leverage AI more efficiently.”
Some of the telco use cases Hispasat is looking at include network resource management and dynamic routing. For example, Ruiz described a scenario where AI could optimize antenna usage by finding patterns in historic usage data to anticipate bottlenecks or maximize cost savings. As is happening in telecommunications, AI could also be used to classify critical communications and dynamically prioritize traffic. AI will also play a role in self-healing networks that dynamically reroute traffic or fail over to alternative pathways in the event of standard path failure.
The Era of the API
One of the great benefits of generative AI is having anytime access to expert-level problem-solving. In the past two to three years, there has been a growing trend toward custom or private Generative Pre-Trained Transformers (GPTs) to support niche needs. Beyond OpenAI’s GPT store, companies are training private GPTs on proprietary data and making them accessible via APIs.
“That is basically the evolution of GenAI as it goes forward,” said Thorsten Stremlau, Systems Principal Architect at Nvidia and a working group co-chair at Trusted Computer Group. While some question the business model, others see it as hugely valuable, especially in industries with highly specialized knowledge.
Stremlau explained how ground system vendors, for example, could train proprietary AI models to provide crucial information and explanations to customers, contractors and the like. “If there’s a vendor doing materials manufacturing or antenna forming, you can book those into your company and now you can leverage their knowledge,” he said.
The ability to share this data in a secure, authorized way not only promises to improve industry knowledge but also can be used to develop even more robust AI models that can preempt service disruptions, manage spectrum resources, support space traffic management and support national security.
“The age of the API is here,” said Mudge. “We’re handing data back and forth between industries all the time, at scale, very quickly and very easily.”
Sharing verified information via secure APIs can unlock new potential applications, including training and building more capable models. According to Mudge, Speedcast currently provides APIs to customers to pull relevant data about their sites.
Keep Your Data Close and Your Models Closer
Having a well-trained proprietary AI model is an incredibly powerful tool. But like any new technology, it comes with risk. According to a 2025 study by McKinsey, cybersecurity, data inaccuracy and privacy were among the top concerns about AI reported by U.S. workplace respondents.
Experts in the satellite community are similarly concerned about data security and privacy risks associated with LLMs. This is especially true of open-source models like ChatGPT that store user inputs and may use them for future training.
Stremlau identified several critical areas of security for companies to consider in developing and deploying private, in-house AI models. These considerations include data sourcing, continuous model improvement, addressing AI hallucinations—unintended and erroneous pattern generation the AI presents as fact—and perhaps most critically, protecting the investment. Depending on the complexity of the model and other variables, a proprietary AI model can cost anywhere from several hundred thousand dollars to over $100 million. “You don’t want that model to now leave your company,” Stremlau emphasized. “So you need to protect that model once you’ve trained it.”
Easier said than done? Perhaps. But while industry insiders are understandably concerned about AI and Gen AI from a security standpoint, they are also looking at it as a security solution. Cybersecurity and network security are other key areas where AI and Gen AI are being used to advance industry interests, including modeling threat surfaces and vulnerabilities and employing AI agents as white hat hackers.
As companies further explore the use of AI in ground systems, its role in improving both operational efficiency and security will become more apparent. While many are just beginning this journey, the growing number of possible use cases suggest AI will be a significant part of the industry going forward.
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