Runtime AI governance platforms for 2026: control plane enforcement

Updated August 21, 2026

TL;DR: Runtime AI governance platforms for 2026 enforce policy at the moment of every model or agent call, inside your own infrastructure, not at a third-party cloud endpoint after the fact. Prediction Guard is a self-hosted sovereign AI control plane that enforces AIUC-1, NIST AI RMF, OWASP, and EU AI Act policies before model responses return, generating SIEM-ready audit logs inside your own environment. Competitors including Noma, WitnessAI, HiddenLayer, Lakera Guard, and Guardrails AI route enforcement or telemetry outside that perimeter, or cover only part of the agentic governance surface, see the comparison below.

A policy that exists in a document but isn't enforced at the system level isn't a control. It's a liability waiting to surface in the next audit cycle, when an engineer under delivery pressure skips the review step. For Chief Information Security Officers (CISOs) and risk leaders in manufacturing, financial services, and defense-adjacent operations, the gap between documented policies and enforced controls is where audit findings are born. Agentic AI exposure is growing faster than the governance programs meant to contain it, and most tools on the market today still process traffic outside your perimeter.

How runtime enforcement secures AI workflows

Before evaluating platforms, it helps to understand the architectural distinction that separates runtime enforcement from retrospective monitoring.

Runtime enforcement means the control plane intercepts every agent call, evaluates it against governance policy, and either allows, blocks, or rewrites the response before it reaches downstream systems, generating an audit log as evidence that enforcement happened. This is structurally different from logging what happened after an agent interaction completes and analyzing it later, which is the architecture most enterprise teams currently operate.

Control-plane vs. data-plane enforcement

In AI systems, the data plane handles model inference, tool invocations through Model Context Protocol (MCP) servers, and agent output generation. The control plane governs which interactions are permitted, under which policies, and with what data, before execution completes. When governance lives at the data plane or outside the perimeter entirely, enforcement becomes retrospective. An external gateway can filter traffic, but the traffic, including any sensitive data in the prompt, has already transited a third-party endpoint.

A control plane enforces at the system level, before the call completes, inside your own infrastructure.

This architectural difference matters to an AIUC-1 assessor or an Office of the Comptroller of the Currency (OCC) examiner reviewing AI governance for a financial services institution. AIUC-1 maps its six pillars (Data and Privacy, Security, Safety, Reliability, Accountability, and Society) across the EU AI Act, NIST AI RMF, OWASP LLM Top Ten, OWASP AI Vulnerability Scoring System (AIVSS), ISO/IEC 42001, and MITRE ATLAS, and the controls it requires are preventive, not detective.

Ensuring audit-ready AI compliance

ISO/IEC 42001 A.6.2.8 requires that event log recording is enabled across AI system lifecycle phases, capturing prompts, tool invocations, outputs, and affected resources as a replayable trace bound to users, sessions, and data sources. A control plane that enforces policy in real time automatically generates governance and enforcement event logs as a byproduct of every governed interaction, creating audit-ready documentation before a review is scheduled. The NIST AI RMF implementation playbook we publish details how structured audit log output maps directly to the Govern, Map, Measure, and Manage functions.

An engineer under delivery pressure does not follow a documented guideline consistently. A control plane enforces the same policy on every call, regardless of the Software Development Kit (SDK) or framework the developer chose.

Defining metrics for 2026 AI security controls

The metrics that matter are architectural and framework-specific, not feature-list summaries that mean different things across vendors.

Evaluating runtime AI governance platforms requires moving beyond feature lists. The metrics that matter to security and risk leaders preparing for an EU AI Act notified body review or your next security review are architectural, not promotional.

Automating real-time governance controls

The OWASP Agentic Applications Top 10 covers ten categories (ASI01 through ASI10) spanning agent goal hijack, tool misuse, identity and privilege abuse, and cascading failures. Documenting these risks in a policy wiki is not equivalent to blocking them at runtime. The agentic AI threats video covers how control-plane automation addresses the categories where manual review fails, including ASI02 (Tool Misuse and Exploitation) and ASI08 (Cascading Failures).

Mapping policies to framework controls

Governance controls must trace back to named framework functions and control IDs. A platform claiming "NIST AI RMF aligned" without a published mapping table gives an enterprise procurement reviewer nothing auditable. The NIST AI RMF mapping guide provides the specific cross-walk published for enterprise buyers. AIUC-1 is the primary cross-framework anchor for multi-regulator environments, with crosswalks mapping to the EU AI Act, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, OWASP LLM Top Ten, OWASP AIVSS, OWASP Top 10 for Agentic Applications (2026), IBM AI Risk Atlas, Cisco AI Security & Safety Framework, CSA AI Controls Matrix, and regional US legislation.

Automated audit log generation and deployment model

ISO/IEC 42001 A.6.2.8 requires lifecycle-phase logging for AI systems. Prediction Guard's audit log records governance events and enforcement decisions on every governed call, not a full replayable trace bound to users, sessions, and data sources. A platform that generates this log automatically on every governed call, in a SIEM-compatible structured format, satisfies this control without requiring compliance teams to manually assemble evidence. Prediction Guard forwards audit logs natively to Splunk, Datadog, Grafana, or syslog, kept entirely within your own environment.

External cloud-hosted gateways introduce a consistent risk profile for regulated enterprises: AI traffic, including sensitive data in prompts or retrieved documents, transits a third-party infrastructure. For organizations handling sensitive or regulated data subject to the EU AI Act or sector-specific data handling requirements, this transit creates a data sovereignty gap. It is also a compliance liability. A self-hosted control plane deployed inside your own infrastructure keeps governance logic, policy enforcement, and audit logs within your environment.

2026 AI governance platform landscape

The platforms below are evaluated on deployment architecture, data sovereignty posture, framework coverage, and agentic runtime enforcement scope.

Guardrails AI: open-source validation framework

Guardrails AI is an open-source framework (Apache-2.0, Python and JavaScript) that has expanded its positioning toward building, governing, and scaling production AI applications beyond its original input/output validation focus. For development teams, the zero-cost open-source foundation and broad validator library are practical entry points. For regulated enterprises requiring system-level runtime enforcement, enterprise governance infrastructure, and native SIEM forwarding, these capabilities are not prominently documented in public-facing sources at the time of writing. Guardrails AI addresses individual layers, not the full agentic governance surface that an AIUC-1 assessor or your next security review will evaluate.

HiddenLayer: model-layer depth and agentic scope

HiddenLayer brings deep model-layer security expertise through its AI Detection and Response (AIDR) capability, covering adversarial attack simulation, model scanning, and runtime model security. The company extended its architecture toward agentic runtime security and MCP governance in 2026, providing real-time agent visibility and guardrails. For enterprises requiring governance across the full agentic stack, including multi-vendor model composability, multi-agent coordination, and workflow-level policy enforcement across the OWASP Top 10 for Agentic Applications (2026), scoping the extent of HiddenLayer's agentic coverage against your specific deployment requirements is an important part of the vendor evaluation process. The specific differentiator for procurement scoping is documentation: Prediction Guard publishes an explicit OWASP Top 10 for Agentic Applications (2026) mapping table that enterprise procurement reviewers can validate line by line, while HiddenLayer's equivalent agentic coverage mapping is not confirmed in publicly available sources at the time of writing.

Lakera Guard: point-solution acquired by Check Point

Lakera Guard was acquired by Check Point in September 2025 and is now part of Check Point's AI security platform. Its prompt-layer filtering capability evaluates AI interaction content for injection and data leakage risks. Check Point's enterprise offering includes a self-hosted deployment option. Regulated enterprises evaluating this capability should confirm current deployment architecture and data routing directly with Check Point, as the post-acquisition product scope and hosting options are evolving at the time of writing. Lakera's product structure spans two distinct capabilities: Lakera Red covers pre-deployment posture assessment, while Lakera Guard provides real-time runtime enforcement across LLMs, AI agents, and multimodal workflows, with coverage extending to MCP. Regulated enterprises evaluating this capability should confirm current AIUC-1 mapping status, audit log sovereignty posture, and deployment architecture directly with Check Point, as the post-acquisition product documentation is evolving at the time of writing.

Noma Security: evaluating runtime security controls

Noma Security provides a comprehensive AI security and governance platform covering discovery, access control, posture management, and runtime security, with Copilot Studio and Salesforce AgentForce integrations extending coverage to common enterprise AI surfaces.

The critical architectural limitation for regulated enterprises is routing. Noma's Kong Gateway plugin requires outbound HTTPS access on port 443 to api.noma.security, meaning telemetry and security event data transit Noma's external infrastructure for processing and analysis. For organizations where no AI interaction metadata can transit a third-party endpoint, this is the controlling architectural characteristic. Noma states coverage of the OWASP Top 10 for Agentic Applications (2026), though the specific coverage scope and level designation cannot be independently confirmed from publicly available sources at the time of writing. Where Noma offers platform breadth, Prediction Guard keeps enforcement and telemetry inside your perimeter without routing to any external vendor infrastructure.

WitnessAI: network-level governance and data sovereignty scope

The platform includes an organization-wide approved-tool list for MCP servers, enforced at the network level, with granular audit logs generated for compliance obligations. WitnessAI uses single-tenant architecture with customer-controlled encryption (BYOK) and multi-region deployment to address data sovereignty requirements. Framework coverage details, including AIUC-1 mapping status, are not prominently documented in publicly available sources at the time of writing.

WitnessAI's own materials describe its deployment as single-tenant isolation with customer-controlled encryption, not explicitly as on-premise or in-customer-VPC hosting. Where WitnessAI's infrastructure sits relative to the customer's own environment isn't clearly specified in primary sources. Prediction Guard, by contrast, explicitly markets its control plane as deploying "directly within your own infrastructure." Given that asymmetry in what each vendor documents about itself, a routing or data-residency comparison between the two platforms can't be stated as settled fact for WitnessAI. It's a question to raise with them directly, not a documented differentiator.

Prediction Guard: self-hosted control-plane enforcement

Prediction Guard is a self-hosted sovereign AI control plane that enforces AIUC-1, NIST AI RMF, NIST AI 600-1, OWASP LLM Top Ten, OWASP Top 10 for Agentic Applications (2026), EU AI Act, and ISO/IEC 42001 policies on every model and agent call before the response returns. You deploy the control plane inside your own infrastructure, whether self-hosted, in a cloud Virtual Private Cloud (VPC), or in an air-gapped environment.

Developers don't change their application code. Existing OpenAI-compatible (/chat/completions, /responses) and Anthropic-compatible (/messages) SDK calls continue working unchanged. Developers repoint only the base_url to the control plane endpoint. Security and Governance, Risk, and Compliance (GRC) teams configure governance policy in the Admin Console. The unified AI strategy video covers how this separation of duties operates across fragmented model and tool ecosystems.

Prediction Guard provides native SIEM integration with Splunk, Datadog, Grafana, and generic syslog forwarding, configured on the Monitor page of the Admin Console. Prediction Guard never holds your SIEM credentials or HTTP Event Collector (HEC) tokens. The control plane formats structured audit log output and your existing ingestion pipeline handles delivery.

Evidence collection for AI governance audits

Building an audit-ready evidence package requires the right log structure, the right framework mapping, and the right deployment model.

Runtime governance policy mapping and framework coverage

Every runtime enforcement event generates a structured audit log entry that maps directly to the policy it enforced, connecting a specific agent interaction to the governance control that evaluated it, the framework function it satisfies, and the outcome: allowed, blocked, or rewritten. For regulated enterprises, that evidence chain must remain inside your own perimeter to be defensible. Practical AI episode 360 covers why perimeter-bound enforcement is insufficient for agentic systems and what Zero Trust principles look like applied to AI agents.

AIUC-1 is the primary cross-framework anchor for multi-regulator environments, and runtime enforcement maps directly to its six pillars. PII masking and prompt-level filtering before model calls complete are the kind of control the Data and Privacy pillar is aimed at, covering data leakage risks across the AI system lifecycle. Prompt injection detection and MCP tool governance align with the scope of the Security pillar, which addresses adversarial inputs and unauthorized tool invocations. Structured audit log generation on every governed call, capturing the policy evaluated, the governance owner, and the outcome, is the kind of evidence the Accountability pillar is designed to produce. For the specific control definitions under each pillar, see aiuc-1.com/crosswalks.

As one example of that mapping, the four NIST AI RMF functions align as follows: Govern maps to Admin Console policy configuration. Map maps to AI System registration and AIBOM export. Measure maps to continuous runtime enforcement and SIEM-forwarded event logs. Manage maps to real-time blocking and rewriting of policy violations before they reach downstream systems.

ASI01 (Agent Goal Hijack) addresses scenarios where attackers redirect agent objectives through malicious tool outputs or poisoned external data. Preventing this category requires runtime enforcement that evaluates agent calls before execution completes, not after the interaction has already returned a result.

Generating SIEM-ready audit evidence

Structure audit logs for native SIEM consumption without requiring the governance platform to hold SIEM credentials. On the Prediction Guard Monitor page, you select your integration target (Splunk, Datadog, Grafana, CrowdStrike, or syslog) and confirm the configuration. The control plane formats audit log output to match your SIEM's native field structure. Your existing ingestion pipeline, whether a Splunk HEC endpoint, a Datadog agent, a Grafana instance, or a syslog collector, handles delivery under your own controls. Prediction Guard does not store SIEM API keys, HEC tokens, or endpoint credentials.

Self-hosted control plane integration and scale

Deploying the control plane directly inside a self-hosted Kubernetes environment keeps AI interaction data within your own infrastructure, so governance logic, enforcement events, and audit logs do not route to external vendor endpoints. The governance logic is CPU-only and does not require GPU resources to enforce policies, which matters for organizations evaluating infrastructure cost in air-gapped environments. The manufacturing deployment video covers the air-gapped deployment architecture for regulated industrial environments.

Because policy enforcement and audit log generation are byproducts of every governed call, the evidence package accumulates continuously. When an OCC examiner or an AIUC-1 assessor requests the AI governance record for a defined period, the SIEM already contains it, without a sprint to assemble evidence.

Evaluating runtime governance vendor capabilities

The tables below compare deployment architecture, data sovereignty, and framework coverage across the primary platforms in this evaluation. Coverage entries marked "Not confirmed" reflect the absence of publicly available vendor documentation at the time of writing, not a confirmed absence of capability.

Deployment architecture and data sovereignty

Reported framework coverage and runtime controls

Comparing runtime enforcement architectures

An internal control plane intercepts every model and agent call before it completes, evaluates it against governance policy inside your perimeter, and returns the result. An external proxy gateway routes that call outside your perimeter for evaluation, introduces a network hop, and routes telemetry to the vendor's infrastructure. That difference determines where enforcement happens, where audit evidence is generated, and who controls the evidence trail. An audit log that lives outside your perimeter is evidence you don't control, and that gap doesn't close through contractual vendor assurance.

Audit log persistence, SIEM integration, and data sovereignty

The vendor must not store audit logs. The control plane generates structured log output. Your SIEM stores and retains it. Any architecture where the governance vendor holds audit data introduces a third-party data custody risk that regulated enterprises cannot accept. Ask every vendor explicitly: where is log data written, who holds the storage credentials, and what happens to your audit log if the vendor experiences a service disruption. Vendor-locked governance also ties configuration to a single cloud provider's ecosystem. Swap model providers or cloud regions, and you rebuild governance from scratch. Self-hosted control planes govern models from any vendor under the same policy framework, and governance configuration doesn't depend on the cloud provider you're using. The product comparisons page covers how the model-agnostic architecture addresses this for multi-cloud and multi-model environments.

Key requirements for enterprise AI control planes

Regulated enterprises share a common set of requirements that determine whether a control plane survives contact with an examiner.

Proven AI runtime use cases for regulators

Presenting runtime controls to an OCC examiner reviewing AI in financial services requires demonstrating that every model interaction with customer financial data was evaluated against policy and that the evidence is inside your perimeter. For defense-adjacent operations, your next security review will require demonstrating that no sensitive or regulated data transited an unauthorized external endpoint during AI processing. Both requirements are met by a self-hosted control plane that enforces policy before calls complete and generates SIEM-ready logs inside your environment.

Preventing outbound data leakage

PII masking, toxicity filtering, and prompt injection detection operate at the control plane level before the model call completes. Sensitive data, whether personally identifiable information, proprietary manufacturing intellectual property (IP), or other regulated data categories, is masked, filtered, or blocked before any model or external tool processes it. These controls map to OWASP LLM02 (Sensitive Information Disclosure) and AIUC-1's Data and Privacy pillar, which the platform's controls are aimed at addressing across the AI system lifecycle.

SIEM integration and verified peer deployments

You configure SIEM integration on the Monitor page of the Admin Console by selecting your target (Splunk, Datadog, Grafana, CrowdStrike, or syslog) and confirming the configuration. The control plane formats audit log output to match your SIEM's native field structure, and Prediction Guard never holds API keys or stores any log data. Audit log retention is your SIEM's responsibility entirely.

Noblis, a defense-adjacent research and engineering organization, is both an investor in and a customer of Prediction Guard, deploying AI inside their own infrastructure boundary for high-security mission contexts. Noblis CEO Mile Corrigan described the strategic alignment as offering "significant opportunities for strategic collaboration on secure deployment of AI systems, including through Noblis' Artificial Intelligence Assurance Implementation (AI2) solution for AI safety," as reported by Washington Technology following the July 2025 investment announcement.

Resolving governance gaps for regulated AI deployments

Design-time policies documented in wikis and reviewed at architecture gates fail when agent calls multiply faster than human review cycles can track them. Runtime enforcement operates at the call level, every time, regardless of how many agents are running or how quickly engineering teams ship new integrations. The gap between design-time documentation and runtime enforcement is where ungoverned agent interactions accumulate into audit findings.

Every runtime control maps to at least one NIST AI RMF function and at least one OWASP item. Prompt injection defense addresses OWASP LLM01 and ASI01. PII masking addresses OWASP LLM02 and AIUC-1's Data and Privacy pillar. Access controls address ASI02 (Tool Misuse) and ASI03 (Identity and Privilege Abuse). The governance policy mapping guide provides this cross-walk in structured form for enterprise procurement review.

Schedule a deployment scoping call to evaluate whether the self-hosted control plane fits your infrastructure and risk requirements. The NIST AI RMF capability mapping whitepaper reviews which framework functions Prediction Guard addresses at the system level before that conversation.

FAQs

Where are audit logs stored in a runtime governance platform?

Audit logs must be generated by the control plane inside your perimeter and forwarded to your SIEM (Splunk, Datadog, Grafana, or generic syslog). Prediction Guard does not store or retain audit logs: the control plane generates structured output, and your existing SIEM ingestion pipeline handles storage, retention, and search entirely within your own environment.

Does runtime policy enforcement introduce latency?

External gateways route traffic outside your perimeter to a vendor's cloud infrastructure, introducing a network round-trip on every call. Because the CPU-only control plane runs locally within your VPC or self-hosted environment, no traffic transits an external vendor endpoint, so enforcement adds no external network transit.

Can we enforce policies across multiple model providers?

Yes. Prediction Guard is model agnostic, enforcing unified AIUC-1, NIST AI RMF, and OWASP policies across open-source, self-hosted, and closed-vendor model endpoints without requiring application code changes.

Key terms glossary

Sovereign AI control plane: A self-hosted governance infrastructure that runs inside your own environment to secure, route, and log all AI interactions without data transiting vendor infrastructure.

Runtime enforcement: The active evaluation and blocking or rewriting of AI inputs and outputs at the API level before the model call completes, generating an audit log as evidence that enforcement happened.

Agentic AI exposure: The risk associated with autonomous AI agents executing unauthorized tool calls, accessing sensitive data, or completing model interactions without system-level controls in place to evaluate and block violations.

SIEM: Security Information and Event Management, a centralized platform that collects, analyzes, and retains security event logs from across an organization's infrastructure for compliance and incident response.

AIUC-1: A voluntary standard for AI agents that maps safety, security, and accountability controls across multiple regulatory frameworks including the EU AI Act, NIST AI RMF, OWASP LLM Top Ten, OWASP AIVSS, OWASP Top 10 for Agentic Applications (2026), ISO/IEC 42001, and MITRE ATLAS, with crosswalks published at aiuc-1.com/crosswalks.

AIBOM: An AI Bill of Materials in CycloneDX format, produced as the exportable artifact of AI System registration, capturing models, MCP servers, datasets, and dependencies for audit and supply chain review.