Updated August 18, 2026
TL;DR: If your AI governance strategy relies on reviewing logs after the fact, you are conducting autopsies, not enforcing policy. True governance requires active runtime enforcement inside your own infrastructure, where every agent call is intercepted, evaluated, and allowed, blocked, or rewritten before the response returns. A self-hosted sovereign AI control plane enforces AIUC-1, National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), and Open Worldwide Application Security Project (OWASP) policies at the call point and keeps all data and audit evidence inside your trust boundary, not a vendor's.
Regulated enterprises in manufacturing, financial services, defense-adjacent operations, and aerospace cannot afford to discover an AI policy violation after the fact. When an agent executes an unauthorized tool call, exfiltrates intellectual property, or passes personally identifiable information (PII) to an external model endpoint, the damage is done before any log review surfaces it. This article explains why active runtime policy enforcement is the only defensible control for agentic AI, maps system-level interception to AIUC-1, NIST AI RMF, and OWASP, and shows how a self-hosted control plane keeps data, enforcement logic, and audit logs entirely within your environment.
The distinction begins with sequence: where in the call lifecycle does the policy decision happen, and who controls that decision point.
To understand the structural difference between runtime enforcement and retrospective log analysis, ask one question: does the policy decision happen before or after the model call completes?
Retrospective analysis follows this sequence: the model call executes, a response generates, a log writes to an external system, and your team discovers the violation during the next review cycle. By then, a PII exposure has already happened, a prompt injection has already reached the model, or an agent has already accessed a resource it was never authorized to touch.
Runtime enforcement reverses this sequence. The control plane intercepts every request at the application programming interface (API) level, evaluates it against the governance policies your security team configured in the Admin Console, and issues a decision to allow, block, or rewrite, all before the model call completes. The audit log records what the system decided: allow, block, or rewrite. It is evidence of the control, not the control itself. Prediction Guard's runtime governance architecture applies this sequence to every agent and model interaction passing through the system.
When you present a control to an OCC examiner, an FDIC examiner, an AIUC-1 assessor, or the lead for your next security review, they will test it against three requirements: is it automated, does it operate at the system level, and is it independent of individual developer compliance?
Document-based governance fails all three. A policy written in a wiki and communicated through training depends on every engineer reading it, understanding it, and applying it consistently under delivery pressure. That is not a control: it is an expectation.
Prediction Guard enforces separation of duties to address this gap. Security and GRC teams configure AI governance policies on the Govern page of the Admin Console once. Those policies then apply to every model or agent call across every registered deployment, regardless of which framework the developer chose or which team shipped the code. Developers do not touch governance configuration, and governance does not depend on developer behavior.
The operational objection most frequently raised by chief information security officers (CISOs) is latency: "Will active runtime enforcement introduce unacceptable delays to our production workflows?" Prediction Guard's control plane is central processing unit (CPU)-only, and each governed call adds a policy evaluation step before model access, and that overhead is small relative to model inference latency. The CPU-only architecture also means you can deploy the control plane inside environments where graphics processing unit (GPU) infrastructure is constrained or air-gapped, including defense-adjacent workloads handling sensitive or classified data.
Before evaluating runtime enforcement tools, assess where your current governance approach sits on the maturity spectrum. The table below contrasts manual, document-based governance with automated, system-level enforcement across six dimensions that matter most to an OCC examiner, an FDIC examiner, an AIUC-1 assessor, or the lead for your next security review.
Governance gap self-assessment
| Governance dimension | Manual / spreadsheet-based | Automated / runtime-enforced |
|---|---|---|
| AI asset inventory | Self-reported by engineering teams, updated manually | AI System registration captures models, Model Context Protocol (MCP) servers, and tools at deployment |
| Policy application | Engineers apply policy based on documentation | Control plane enforces policy on every call, independent of developer behavior |
| Audit evidence | Assembled from email threads and portal exports before each review | Generated continuously as a byproduct of runtime enforcement, security information and event management (SIEM)-ready |
| PII protection | Depends on developer awareness and code review | Detected, masked, or blocked at the API level before model access |
| Vendor telemetry routing | Varies by tool, often external | Entirely inside customer VPC or air-gapped environment |
| Evidence freshness | Stale between manual updates | Current as of the last governed interaction |
Understanding why logs fail as controls requires separating what a log records from what a control prevents, which are two structurally different capabilities.
Once an agent executes an unauthorized tool call or passes regulated data to an external endpoint, the downstream consequences cannot be undone by writing a better log entry. The lead for your next security review will ask whether your controls prevented the violation, not merely whether your logs recorded it.
A log entry that shows a policy violation occurred without being blocked is evidence of a control failure, not evidence of a control. For regulated AI workloads, an audit log only satisfies an assessor if it demonstrates that a governance decision was active at the moment of execution. ISO/IEC 42001 Annex A Control A.6.2.8 requires event log recording for AI systems across the full lifecycle, capturing prompts, tool invocations, outputs, and affected resources as a replayable trace bound to users, sessions, and data sources. That replayable trace has to show what the control plane decided, not just what the model produced.
Retrospective auditing creates a second structural problem: the log only captures what the system was configured to record. If the logging configuration does not cover a new agent type, a new MCP server integration, or a new model endpoint, those interactions are invisible until the gap is discovered, often during an audit. Runtime enforcement at the control plane level applies uniformly to every registered interaction regardless of how the application was built. The EP03: Agentic AI Automation video covers why agent-level threats require interception at the call point.
The interception architecture is the technical foundation of the governance argument. Here is exactly how requests are evaluated before model access occurs.
Developers point their existing OpenAI-compatible (/chat/completions, /responses) or Anthropic-compatible (/messages) software development kit (SDK) calls at the Prediction Guard control plane endpoint by changing only the base_url. No application code changes. No SDK replacement. The control plane intercepts the call locally, evaluates it against governance policy, and returns either the governed response or an enforcement action record, all inside your infrastructure, inside your virtual private cloud (VPC) or air-gapped environment.
Compare this to external gateway architectures. Noma Security's Kong Gateway plugin requires outbound HTTPS to api.noma.security on port 443 in its default configuration, placing enforcement decisions and telemetry outside the customer's perimeter. For regulated workloads where every governance artefact must remain inside the trust boundary, that routing architecture is a structural disqualifier regardless of how strong the policy logic is. The Noma architecture assessment covers this distinction for procurement teams doing side-by-side evaluations.
The control plane applies validation to every call, covering input scanning, policy checks, and output validation. Input scanning flags adversarial inputs and detects PII before it reaches the model. Policy checks verify that the requesting agent or user is authorized to call the target model or MCP server. Output validation applies toxicity filtering and grounding verification against trusted reference data sources before the response returns to the application.
Rule-based controls such as access policies and usage quotas apply deterministically. Grounding verification is probabilistic, flagging outputs that diverge from trusted reference documents rather than providing a binary guarantee. The EP04: OWASP guidance for AI security video walks through how these validation steps map to OWASP LLM Top Ten items in production deployments.
Your security team configures all enforcement rules on the Govern page of the Admin Console without writing code. Rules apply uniformly to every model and agent registered in the system, across every vendor endpoint and self-hosted model, under one policy framework. Governance configuration is isolated from developer workflows, so policy changes do not require a code deployment, and code deployments do not accidentally override governance rules.
After a call completes, the control plane generates structured audit log output formatted natively for your existing SIEM. Prediction Guard does not hold SIEM credentials, API keys, or HTTP Event Collector (HEC) tokens. It configures output formatting only. Your existing ingestion pipeline, whether a Splunk HEC endpoint, a Datadog agent, or a syslog collector, handles delivery under your own controls. The token usage monitoring architecture follows the same credential-free pattern, routing telemetry into your observability stack without introducing a new credential surface.
Mapping enforcement capabilities to specific NIST AI RMF functions closes the gap between framework alignment claims and auditable, system-level evidence.
AIUC-1 crosswalks to NIST AI RMF, ISO/IEC 42001, and OWASP simultaneously, making it the most efficient single anchor for multi-framework compliance evidence. NIST AI RMF 1.0 is one of the frameworks AIUC-1 maps to, structured around four core functions: Govern, Map, Measure, and Manage. Runtime enforcement at the call point directly supports the Measure and Manage functions by generating structured evidence that governance controls operated on every interaction. The NIST AI RMF implementation playbook covers how to map these functions to specific deployment controls across infrastructure, platform, and application layers.
The table below maps Prediction Guard's core runtime enforcement capabilities to AIUC-1 pillars and NIST AI RMF functions. AIUC-1 crosswalks to NIST AI RMF, ISO/IEC 42001, and OWASP simultaneously, making it the most efficient single anchor for multi-framework compliance evidence.
AIUC-1 and NIST AI RMF runtime enforcement mapping
| Prediction Guard capability | AIUC-1 pillar | NIST AI RMF function |
|---|---|---|
| Prompt injection detection | Security | Manage |
| PII detection and masking | Data and Privacy, Security | Govern, Manage |
| Model and MCP server permission enforcement | Security, Accountability | Govern, Manage |
| Output grounding verification against trusted reference sources | Reliability, Safety | Measure, Manage |
| AI System registration with exportable AIBOM in CycloneDX format | Accountability | Map, Govern |
| Runtime enforcement audit log generation with SIEM-native output formatting | Accountability, Security | Govern, Manage |
The practical value is that when every model call is evaluated against NIST-mapped governance policies and the result is logged inside your own infrastructure, your compliance team does not need to assemble a new evidence package at the start of each audit cycle. The evidence is generated continuously as a byproduct of operational enforcement, and the AIUC-1 crosswalk at aiuc-1.com/crosswalks provides the multi-framework view covering NIST AI RMF, ISO/IEC 42001, and OWASP together.
Runtime enforcement addresses the OWASP Top 10 for Agentic Applications 2026 threat categories at the call point, before adversarial inputs reach the model, unauthorized tool calls execute, or sensitive outputs reach the user. The table below maps Prediction Guard's capabilities to all ten ASI items, with AIUC-1 pillar alignment drawn from the confirmed per-risk crosswalk.
OWASP Top 10 for Agentic Applications runtime enforcement mapping
| OWASP item | Threat vector | Prediction Guard capability | AIUC-1 pillar |
|---|---|---|---|
| ASI01: Agent Goal Hijack | Adversarial inputs or instructions redirect the agent's objective away from its intended goal | Prompt injection detection and input scanning at API level, before model access | Security |
| ASI02: Tool Misuse and Exploitation | Agents invoke tools outside their intended scope or in ways that cause unintended harm | Model and MCP server permission enforcement, access controls | Security, Accountability |
| ASI03: Identity and Privilege Abuse | Agents assume elevated privileges or impersonate users and other agents to access unauthorized resources | Model and MCP server permission enforcement, agent identity validation | Security, Accountability |
| ASI04: Agentic Supply Chain Vulnerabilities | Compromised models, tools, plugins, or dependencies introduce risk into the agent workflow | AI System registration with exportable AIBOM in CycloneDX format | Accountability, Security |
| ASI05: Unexpected Code Execution (RCE) | Agents execute arbitrary or malicious code as a result of adversarial inputs or tool misuse | Input scanning and policy validation applied before tool call execution | Security |
| ASI06: Memory & Context Poisoning | Adversarial content corrupts agent memory or context stores to manipulate future behavior | Input scanning and policy validation applied to memory-bound inputs before model access | Security |
| ASI07: Insecure Inter-Agent Communication | Malicious or compromised agents manipulate messages passed between agents in multi-agent systems | Model and MCP server permission enforcement, inter-agent call validation | Security, Accountability |
| ASI08: Cascading Failures | A failure in one agent or tool propagates uncontrolled through dependent agents and workflows | Access controls, usage limits, and enforcement boundaries isolate failures at the call level | Reliability, Safety |
| ASI09: Human-Agent Trust Exploitation | Agents exploit user trust to elicit sensitive information, execute unauthorized actions, or bypass oversight | Output grounding verification and enforcement actions that flag unverified or manipulative outputs before they reach users | Security, Safety, Reliability |
| ASI10: Rogue Agents | Agents operate autonomously beyond defined boundaries, pursuing goals or actions outside authorized scope | Access controls, usage limits, and enforcement boundaries that constrain autonomous agent behavior within authorized scope | Reliability, Safety, Accountability |
For deployments scoped to single-model applications rather than agentic workflows, the OWASP LLM Top Ten maps the same enforcement capabilities to LLM01 (Prompt Injection), LLM02 (Sensitive Information Disclosure), LLM06 (Excessive Agency), and LLM09 (Misinformation). The AIUC-1 crosswalk at aiuc-1.com/crosswalks provides the unified view across OWASP LLM Top Ten and OWASP Top 10 for Agentic Applications.
The EP04: OWASP guidance for AI security video walks through how these validation steps apply in production environments. The agentic AI threats overview covers how the ASI categories map to control plane interception in multi-agent deployments.
Each log entry must demonstrate that a governance decision was active at the moment of execution, not merely that an event occurred.
ISO/IEC 42001 A.6.2.8 establishes that organizations must record event logs capturing prompts, tool invocations, outputs, and affected resources as a replayable trace across the AI system lifecycle. For a runtime enforcement control plane, every audit log entry must record what the input was, which policy applied, what the control plane decided, and what the model or agent produced. The enforcement action, allow, block, or rewrite, is what transforms a log from a diagnostic record into auditable governance evidence.
Assessors will also want each entry tied to the policy configuration active at the time of the call, so that changes between audit cycles can be traced and no regression window goes unaccounted for. Confirm which fields your deployment's audit log output includes during scoping.
Prediction Guard formats audit log output to match the native field structure of your SIEM without holding credentials or managing delivery. The Monitor page in the Admin Console lets your security team configure which integration is active, whether Splunk, Datadog, CrowdStrike, or syslog, and the control plane begins formatting output in the corresponding field structure. Your existing ingestion pipeline handles delivery under your own credential management. Prediction Guard is never a credentialed party in your security infrastructure, eliminating a class of vendor supply-chain risk that AIUC-1 assessors and AI underwriters now scrutinize as AI governance programs mature.
All audit logs are generated inside your own infrastructure and retained in your SIEM. Prediction Guard does not retain, store, or search logs on its own systems. Storage, retention scheduling, and search are entirely under your SIEM's control, ensuring no governance artefact leaves the defined perimeter.
The comparison that matters most for regulated workloads is not feature parity but where enforcement happens and who controls the resulting evidence.
Advisory governance, policies documented in wikis and communicated through training, introduces a human step inside an automated workflow. Every manual step is a potential bypass. System-level enforcement removes the human step from the enforcement path entirely. The EP06: Harmonizing your AI tools video discusses why policy fragmentation across multi-vendor AI deployments creates governance gaps that advisory controls cannot close.
The competitive distinction between Prediction Guard and external gateway architectures is not about which policies each tool can enforce. It is about where enforcement happens and where the resulting telemetry routes. Noma Security's Kong Gateway plugin routes telemetry to api.noma.security:443, placing governance artefacts outside the customer's perimeter. Prediction Guard's entire control plane, including enforcement logic and audit log generation, operates inside your VPC or air-gapped environment. For defense-adjacent organizations handling sensitive or regulated data, and for financial services teams under OCC or FDIC examination, the routing difference is the compliance difference. The Noma alternatives analysis covers this in depth for teams doing architectural due diligence.
Hyperscaler point solutions such as AWS Bedrock Guardrails and Azure AI Content Safety apply content filtering within their respective ecosystems, but neither delivers portable multi-vendor governance. The control is bound to traffic that flows through that provider's infrastructure. The moment your deployment includes a model that does not flow through Bedrock, or a self-hosted model in your VPC, you lose visibility and enforcement. Prediction Guard's model and vendor agnostic design governs open-source, closed-vendor, and self-hosted models under one policy framework, and governance configuration is portable across hardware and infrastructure. The EP10: The "USB-C" of AI covers this composability model in practical terms.
Mile Corrigan, Noblis President and CEO, framed the institutional challenge directly following Noblis Ventures' investment in Prediction Guard:
"This alignment offers significant opportunities for strategic collaboration on secure deployment of AI systems, including through Noblis' Artificial Intelligence Assurance Implementation (AI2) solution for AI safety, and further strengthens our ability to help customers navigate AI adoption while safeguarding sensitive data."
For a CISO presenting to an OCC examiner, an FDIC examiner, or an AIUC-1 assessor, the output of a runtime control plane is exactly what that presentation requires: a structured inventory of registered AI assets, a continuous log of governed interactions with enforcement actions recorded, policy version binding on every entry, and SIEM-native formatting that slots into existing security operations workflows.
Structural controls apply to new and existing deployments alike. The sections below cover both retrofitting and audit evidence assembly for regulated environments.
Transparent API compatibility means you can retrofit existing AI applications with system-level governance controls without rewriting application code. For applications built on OpenAI or Anthropic SDKs, or frameworks such as LangChain that use OpenAI-compatible callers, the only change is the base_url. No dedicated package is required. Governance applies from that point forward across every call the application makes, covering models and agents that have been in production for months without system-level governance. Practical AI episode 343, "Controlling AI Models from the Inside," covers why traditional guardrails and input/output filters, the typical way governance gets retrofitted onto existing AI deployments, often prove too slow, too expensive, or too limited in production, and explores model-native runtime signals as an alternative.
The organizations that survive their first formal AI governance review built systems that document themselves as a byproduct of operation, not ones that assembled evidence packages manually in the weeks before the assessor arrived. Runtime enforcement, continuous SIEM-native logging, AI System registration with exportable AIBOM in CycloneDX format (an industry-standard software bill of materials specification), and framework mapping tables tied to AIUC-1, NIST AI RMF, OWASP, and ISO/IEC 42001 together produce an evidence package that is current as of the last governed interaction.
To evaluate whether a self-hosted deployment fits your infrastructure and compliance requirements, book a scoping call to discuss your environment, your regulatory obligations, and what a Prediction Guard deployment produces as evidence for your next assessor review.
Every governed call adds a policy evaluation step before the model call proceeds. Actual overhead varies by deployment environment, workload profile, and policy configuration, and it is small relative to model inference latency. Because the control plane is CPU-only, that evaluation does not compete with model serving for GPU capacity. Confirm performance characteristics for your specific environment during a scoping call.
No. Prediction Guard does not hold or manage SIEM credentials of any kind. The control plane configures the output formatting of the audit logs only, and your existing ingestion pipeline handles secure delivery to your SIEM under your own credential controls.
An audit log records what happened. A governance control determines what is permitted to happen. Prediction Guard enforces policy at the moment of the call and generates the log as evidence that enforcement occurred. When a log shows a violation that was not blocked, that is evidence of a control failure, not a control.
No. Retrospective analysis is a diagnostic tool that tells you what happened after a workflow completed. Runtime enforcement is a preventative control that decides what happens at the moment of execution. For CISOs accountable to an OCC examiner, an FDIC examiner, or an AIUC-1 assessor, a tool that records violations without blocking them provides evidence collection, not risk management.
Your security team can tune thresholds and rules on the Admin Console's Govern page without code deployment, adjusting enforcement sensitivity for specific agent types, model endpoints, or workflow contexts. Starting new rules in logging mode and promoting to blocking mode after confirming the false positive rate is acceptable is the standard approach for production deployments.
Prediction Guard ships applicable governance baselines for NIST AI RMF (Govern, Map, Measure, and Manage functions), NIST AI 600-1, and OWASP Top 10 for LLM Applications items LLM01 through LLM10. For agentic deployments, runtime enforcement maps to OWASP Top 10 for Agentic Applications ASI01 through ASI10. For multi-framework coverage, AIUC-1 crosswalks to NIST AI RMF, ISO/IEC 42001, and OWASP simultaneously; Prediction Guard provides configurable capabilities that support controls within your AIUC-1 and ISO/IEC 42001 program, but does not establish conformity on your behalf. For SOC 2 and HIPAA, the same framing applies: Prediction Guard's runtime enforcement, audit log generation, and data perimeter controls provide configurable capabilities that support controls within your own compliance program. Full crosswalk detail is at aiuc-1.com/crosswalks.
Active runtime enforcement: A security control that intercepts, evaluates, and allows, blocks, or rewrites AI model calls in real time before the transaction completes.
Sovereign AI control plane: A self-hosted software infrastructure that governs and secures interactions between disparate models, tools, and agents entirely within an organization's trust boundary.
Agentic AI exposure: The security and compliance risk associated with deploying autonomous AI agents that can execute tool calls and access data without system-level policy enforcement.
Grounding verification: A probabilistic validation process that evaluates model-generated outputs against trusted reference documents to detect and flag outputs that diverge from those sources before they reach users.
AIBOM (AI Bill of Materials): An exportable inventory of registered AI assets in CycloneDX format, produced as a byproduct of AI System registration. Provides a structured record for compliance reporting and assessor inquiries from OCC examiners, FDIC examiners, AIUC-1 assessors, or your next security review.
Separation of duties: The governance principle that distributes responsibilities between developers and security teams so no single actor controls both application code and governance policy configuration, with the control plane enforcing security team policies on every developer-initiated call.