Nol8 launches AI data plane for enterprise controls

Nol8 has launched its AI Data Plane for commercial use, aimed at companies seeking tighter control over what data reaches artificial intelligence systems.

The platform sits between enterprise data and AI workloads, inspecting data moving into and out of models, agents and tools. It reviews prompts, retrieved documents, tool calls, agent-to-agent exchanges and model outputs, then applies company-defined rules to decide what can pass through.

The launch comes as businesses shift from testing AI tools to deploying larger numbers of autonomous agents across their operations. That shift is prompting closer scrutiny of how companies govern sensitive information exposed to models during routine tasks.

Nol8 says its approach relies on deterministic pattern matching rather than probabilistic controls. That means the same rule should produce the same result on every request, with requests blocked if the platform is unreachable rather than allowed through.

Benchmark results released alongside the launch compared the product with a CPU-based baseline built on Google RE2, a pattern-matching engine. Nol8 says the testing was independently validated and found that its system processed more than 100 times more data per dollar at the top end of the policy range examined.

In the tests, policy size increased from 128 rules to 5,000 rules. Across that range, Nol8 says its platform sustained throughput of 1 GB/s, while the RE2 baseline slowed by 30 times as rule volumes increased.

The company argues this matters because the cost of inspecting AI traffic can rise sharply as organisations add more policies. For businesses trying to govern growing volumes of AI interactions, the economics of inspection infrastructure are becoming part of the adoption debate.

Industry forecasts cited by Nol8 point to sharp growth in agent use. Gartner has forecast that by 2028 the average global Fortune 500 company will have more than 150,000 AI agents in use, up from fewer than 15 in 2025, while IDC has projected a tenfold increase in agent use across the Global 2000 by 2027.

Security focus

Unlike traditional data governance systems centred on stored information, Nol8 is targeting what it describes as data in motion. That includes the live flow of information through prompts, context retrieval, software tool calls and exchanges between agents.

Kelly Herrell, Chief Executive Officer of Nol8, said the issue starts with data access rather than model behaviour.

"Most of the conversation about enterprise AI has focused on whether it behaves correctly. Before behavior comes a more basic question: what data does AI actually access? AI needs broad access to be useful, and that access expands every day as AI pushes deeper into the business," Herrell said.

"With NOL8, companies predetermine what data can and cannot pass into and out of AI systems and enforce it at machine speed, without slowing autonomous workloads. They can give AI the access it needs and still decide exactly what data it receives. That certainty is now available to any enterprise running AI on its data," he added.

Nol8 says the system is available on AWS for production deployments. Customers can manage policies through a web console or a REST API, and the platform supports single sign-on and mutual TLS with automatic certificate rotation.

The company also says customer data is not stored and contents are not logged. Access for existing applications is handled through a data API.

Market backdrop

Security specialists have warned that many existing inspection tools were built before the current surge in AI adoption. Those systems often force a trade-off: scan all traffic and accept performance slowdowns, or sample a smaller portion and accept weaker controls.

Sanjeev Mohan, Principal and Founder of SanjMo, said the challenge is becoming more acute as businesses move beyond pilots.

"Enterprises have spent years governing data at rest with static policies. AI agents work with data in motion: the chain of thought, retrieved context, tool calls, and handoffs moving in and out of models every second," Mohan said.

"Most inspection tools were built for a pre-AI world and force a bad tradeoff: inspect everything and slow the workload, or sample and hope. Putting deterministic inspection directly in the AI data path is the kind of infrastructure the AI era requires. It is also what frees the most valuable AI use cases, the ones stuck in pilots because teams cannot prove what sensitive data is going into a model or coming back out," he added.

Nol8 is a business unit of FortifAI. The platform is intended for enterprises, inference providers and colocation operators that want predictable costs for AI data inspection as policy requirements expand.

Dr. Alon Rashelbach, Co-Founder and Chief Technology Officer of Nol8, said the company sees determinism as a requirement for AI governance.

"Data governance for AI must be deterministic. If a policy says a class of data cannot reach a model, that must hold on every request, not most of them," Rashelbach said.

"The NOL8 AI Data Plane matches patterns rather than inferring intent, so its latency barely moved as the policy grew. Security and compliance teams can control the data AI systems see without engineering teams paying for it in latency or infrastructure sprawl," he added.