NOL8 AI Data Plane™ Delivers Enterprise Data Control Designed for AI

New platform advances AI trust with deterministic enterprise controls that bring 100% certainty to AI data payloads

Highlights

• NOL8 AI Data Plane delivers data control to prevent sensitive information from entering AI workloads at scale
• The platform inspects 100% of data flowing to AI and filters it so only policy-approved data reaches models, agents and tools
• Millisecond-grade performance enables certainty without the penalty of latency while achieving more than 100x better TCO than traditional systems
• Available on AWS and through Megaport, with proof-of-concept engagements open on Equinix Fabric

PHOENIX and SYDNEY, Oct. 28, 2026 /PRNewswire/ -- NOL8 today announced the commercial availability of the NOL8 AI Data Plane™, which brings trust to enterprise AI by deterministically controlling what data reaches AI systems.

AI models and guardrails perform on probability, whereas the NOL8 AI Data Plane performs on determinism. The NOL8 platform inspects every byte of data entering and leaving AI workloads and controls what data reaches each model, agent and tool. Enterprises gain 100% certainty that the data they consider sensitive never reaches autonomous AI systems that could expose it.

The NOL8 AI Data Plane sits in the direct path between enterprise data and AI workloads. It reads every prompt, retrieved document, tool call and agent-to-agent exchange on the way in, and every model output on the way out, applying the enterprise's own data-sensitivity rules. Because it uses deterministic pattern matching, the same rule always produces the same result. And because it runs on accelerated hardware, it inspects full payloads in milliseconds at massive scale. Enterprises get certainty without a latency penalty, at more than 100x better TCO than traditional systems.

Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025¹. IDC predicts that by 2027, agent use across the Global 2000 will increase tenfold, with token and API call loads rising a thousandfold². Every one of those calls touches enterprise data. Enterprises need agents to drive productivity, but they won't let agents near sensitive data until they can control what data those agents see.

"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? AI needs broad access to be useful, and that access expands every day as AI pushes deeper into the business," said Kelly Herrell, Chief Executive Officer of NOL8. "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."

Flat cost as policies grow

Every rule an enterprise adds to its AI data policy buys more certainty. On general-purpose CPUs, every rule also costs throughput, so the infrastructure bill rises with the policy.

In third-party validated benchmark testing, NOL8 compared the NOL8 AI Data Plane with a CPU-based baseline built on Google RE2, a widely deployed pattern-matching engine, as the policy grew from 128 to 5,000 rules³. The NOL8 AI Data Plane sustained 1 GB/s across the full range, whereas the RE2 baseline's throughput fell 30-fold. At 5,000 rules, the NOL8 AI Data Plane delivered 118x more data per dollar.

"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," said Sanjeev Mohan, Principal and Founder of SanjMo. "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."

For enterprises, flat cost per rule makes AI data protection a fixed line item rather than an expense that grows with every rule. For Neoclouds, inference providers and colocation operators that sell throughput per dollar, it opens a governed service tier with a predictable cost per gigabyte.

Built for production

The NOL8 AI Data Plane includes the features enterprises need to run it at scale:

• Centralized policy management via a web console to create, validate, version and publish policies, and to view the status of every site.
• A REST API for managing policies programmatically and deploying them rapidly through CI/CD pipelines.
• Mutual TLS. Every connection between platform components is authenticated on both sides, with certificates that rotate automatically.
• Single Sign-On. Console access through a company's existing identity provider.

The platform works with existing applications through its data API. If the NOL8 AI Data Plane is unreachable, requests are blocked rather than passed through. Customer data is not stored, and its contents are not logged.

"Data governance for AI must be deterministic. If a policy says a class of data cannot reach a model, that must hold on to every request, not most of them," said Dr. Alon Rashelbach, Co-Founder and Chief Technology Officer of NOL8. "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 system sees without engineering teams paying for them in latency or infrastructure sprawl."

Availability

The NOL8 AI Data Plane is now available on AWS for production deployments. Joint proof-of-concept engagements are open to select customers via the Megaport AI Exchange and Equinix Fabric with general availability planned for later in 2026. Organizations that want to test NOL8 AI Data Plane on their own traffic and rules can visit nol8.io for more information.

Footnotes

[1] Gartner, "Gartner Identifies Six Steps to Manage AI Agent Sprawl," press release, April 28, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-identifies-six-steps-to-manage-artificial-intelligence-agent-sprawl

[2] Rick Villars, IDC, "Agent Adoption: The IT Industry's Next Great Inflection Point," blog post, December 10, 2025. https://www.idc.com/resource-center/blog/agent-adoption-the-it-industrys-next-great-inflection-point/

[3] NOL8, independently validated benchmark testing, September 2026. Both systems processed identical inputs and policies under a 9 millisecond P99 latency target. At 5,000 rules, the NOL8 AI Data Plane held P99 latency of 8 milliseconds, demonstrating up to 108x data-per-dollar advantage over Google's RE2. Results reflect one workload profile and one software baseline, and are not presented as the best achievable CPU implementation. Your figures will differ when using your own traffic.

Media Contact: Matt Wolpin • matt@nol8.io
Investor Relations: John Marrin • john.marrin.ir@nol8.io • +61 468 828 878

About NOL8. AI systems make decisions on data that teams often can't fully see or verify in the moment, and NOL8 exists to close that gap. Its technology, the AI Data Plane, gives organizations visibility into how data moves through their AI systems, enabling certainty where there was once assumption. For more information, visit nol8.io.

NOL8 is a business unit of FortifAI Limited (ASX: FTI).

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SOURCE NOL8 USA