NOL8 has launched the NOL8 AI Data Plane, a platform designed to control which enterprise data enters and leaves AI models, agents and tools. The system applies deterministic pattern-matching policies to prompts, retrieved documents, tool calls and model outputs, aiming to give security teams more predictable control over sensitive information as organizations deploy autonomous AI. Available for production deployments on AWS, the platform also targets AI infrastructure providers seeking to offer data-governed inference services with more predictable inspection costs.
NOL8 Launches AI Data Plane to Control Enterprise Data Access
As enterprises connect AI agents to internal documents, business applications and operational systems, controlling what those agents can access is becoming a central infrastructure challenge. Model safeguards can help shape how an AI system behaves, but they do not necessarily provide a deterministic guarantee that sensitive information will never enter a model’s context or leave through its responses.
NOL8 is targeting that gap with the commercial launch of its NOL8 AI Data Plane, a platform that inspects data moving into and out of AI workloads and applies enterprise-defined policies before requests proceed. The company says the system uses deterministic pattern matching rather than probabilistic model-based decisions to control sensitive data across prompts, retrieved documents, tool calls, agent-to-agent exchanges and model outputs.
The product is now available for production deployments on Amazon Web Services (AWS). NOL8 is also opening joint proof-of-concept engagements through Megaport AI Exchange and Equinix Fabric, with broader general availability planned for later in 2026.
Moving Data Governance Into the AI Request Path
Traditional enterprise data governance often focuses on permissions, access controls and information stored in databases or files. AI agents introduce another layer of complexity because they can retrieve information, call tools, pass context between agents and generate outputs continuously.
NOL8 places its AI Data Plane directly between enterprise data and AI workloads. It inspects incoming and outgoing payloads, matching them against policies defined by the organization. The objective is to prevent specified sensitive data from reaching AI systems or appearing in their responses.
The distinction between deterministic and probabilistic controls is central to the product’s positioning. A pattern-matching rule can produce the same result whenever the same matching conditions occur. A model-based safeguard, by contrast, can produce different decisions across similar inputs.
This approach could be useful for organizations that need consistent enforcement of rules covering identifiers, confidential information or other defined data patterns. It does not mean every possible form of sensitive information can be detected automatically. Effective protection still depends on how policies are written, which data formats are supported and whether the rules capture the information an organization needs to protect.
NOL8 says the platform inspects full payloads in milliseconds and uses accelerated hardware to sustain throughput as policy complexity increases. Those performance claims are important because an inspection layer that significantly slows inference could undermine the responsiveness of AI applications.
Benchmark Claims Focus on Scaling Policy Complexity
The company reports that third-party validated benchmark testing compared its platform with a CPU-based baseline using Google RE2, a pattern-matching engine, as the number of rules increased from 128 to 5,000.
According to NOL8, its system sustained throughput of 1 GB/s throughout the test range, while the RE2 baseline’s throughput declined 30-fold. At 5,000 rules, the company says its platform delivered 118 times more data per dollar.
The results address a practical infrastructure issue: data inspection can become more resource-intensive as enterprises add policies for different teams, systems and regulatory requirements. If inspection costs rise sharply with each additional rule, organizations may be forced to limit policy coverage or spend more on processing capacity.
NOL8 argues that its approach makes the cost of enforcing additional rules more predictable. That could appeal to enterprises operating large AI environments, as well as neoclouds, inference providers and colocation operators selling compute services where throughput and unit economics matter.
However, the announcement does not provide the complete benchmark methodology, hardware configurations, workload composition or detailed cost assumptions. The reported figures should therefore be treated as vendor-reported results until organizations can assess them against their own traffic patterns and infrastructure costs.
Designed for Enterprise Operations
The AI Data Plane includes a web console for creating, validating, versioning and publishing policies, alongside a REST API for programmatic management and integration with continuous integration and continuous delivery pipelines.
NOL8 also lists mutual Transport Layer Security (mTLS), automatic certificate rotation and single sign-on through existing identity providers. These capabilities are intended to support authenticated connections between platform components and integrate access management with enterprise identity systems.
One notable feature is its fail-closed behavior. If the AI Data Plane becomes unreachable, requests are blocked rather than allowed to bypass inspection. That can reduce the risk of sensitive data passing through an unprotected path, although it also means an outage could interrupt dependent AI applications.
NOL8 says the platform does not store customer data or log its contents. Enterprises evaluating the product should still review what operational metadata is retained, how policies are administered and versioned, and how exceptions and blocked requests are handled.
The Infrastructure Challenge Behind Agentic AI
The launch comes as businesses explore agents that can perform tasks across enterprise software and data sources. NOL8 cites Gartner’s prediction that the average Fortune 500 enterprise will use more than 150,000 AI agents by 2028, compared with fewer than 15 in 2025. It also cites IDC’s forecast that agent use across the Global 2000 will increase tenfold by 2027, alongside a thousandfold rise in token and API call loads.
These are forecasts rather than current adoption measurements, but they illustrate the scaling challenge the company is addressing: every additional model call, tool invocation or agent handoff creates another opportunity for sensitive information to be exposed.
For enterprise security teams, the potential value lies in enforcing data policies at the point where AI systems consume and produce information. For infrastructure providers, the same mechanism could support a governed inference tier with clearer costs for data inspection.
The remaining test is deployment performance under real-world conditions. Policy accuracy, compatibility with AI frameworks, failure handling and measured latency will determine whether deterministic inspection can deliver consistent protection without undermining the responsiveness that makes agentic systems useful.
Market Landscape
Enterprise AI security is expanding beyond model-level guardrails toward infrastructure controls that govern data access, retrieval and movement between models, agents and tools.
- AI data security: Organizations need controls over sensitive information entering prompts, retrieved context and generated responses.
- Deterministic policy enforcement: Pattern matching can provide consistent results for defined data patterns, but its effectiveness depends on policy coverage and implementation.
- Agentic infrastructure: Multi-agent systems increase the number of tool calls, handoffs and data exchanges that organizations may need to inspect.
- Accelerated inspection: Hardware-accelerated processing could help reduce the throughput costs associated with inspecting large volumes of AI traffic.
- Governed AI infrastructure: Cloud and inference providers may be able to offer customers additional data-protection controls as part of their AI services.
NOL8’s positioning centers on enforcing enterprise data policies directly in the AI request path. Its reported benchmark results suggest a focus on scaling inspection as policy complexity increases, but independent testing under varied workloads will be important to evaluate its real-world performance.
Top Insights
- NOL8 has commercially launched an AI Data Plane designed to enforce enterprise-defined data policies across AI prompts, retrieved content, tool calls and model outputs.
- The platform uses deterministic pattern matching to control specified data patterns rather than relying solely on probabilistic model safeguards.
- NOL8 reports sustaining 1 GB/s across policies ranging from 128 to 5,000 rules, with 118 times more data per dollar than its tested RE2 baseline at 5,000 rules.
- The platform supports policy management through a web console and REST API, with mTLS, certificate rotation, single sign-on and fail-closed request handling.
- Production deployments are available on AWS, while selected proof-of-concept engagements are planned through Megaport AI Exchange and Equinix Fabric.
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