Noma Security vs. Lakera Guard: comparing AI governance platforms for regulated industries

Updated July 21, 2026

TL;DR: Noma Security and Lakera Guard address real enterprise AI risk problems from distinct architectural positions. Noma's Kong Gateway plugin routes policy evaluation to Noma's SaaS infrastructure in its default configuration, creating a sovereignty constraint where AI interaction data transits a third-party endpoint. Lakera Guard provides real-time content moderation and prompt injection detection in two modes: a direct API that returns structured verdicts to the calling application, and a Kong AI Lakera Guard plugin that enforces blocking inline via Kong Gateway. In both modes, enforcement decisions are computed in Lakera's cloud service (api.lakera.ai), creating a SaaS evaluation dependency regardless of which integration mode is used. For regulated enterprises in financial services, defense-adjacent sectors, or manufacturing, Prediction Guard deploys a self-hosted sovereign AI control plane that enforces AIUC-1, NIST AI RMF, and OWASP policies at runtime entirely inside your environment, with audit logs generated inside your perimeter and consumed by your own SIEM.

When an FFIEC (Federal Financial Institutions Examination Council) examiner or DCSA (Defense Counterintelligence and Security Agency) assessor asks for a complete inventory of your production AI systems and their real-time policy enforcement logs, a post-hoc monitoring dashboard or a gateway that routes telemetry to a third-party server will not satisfy the question. Both tell you what happened. Neither prevents violations before they occur inside your environment.

This comparison covers the architectural differences, risk and compliance coverage, deployment models, and sector-specific fit for Noma Security, Lakera Guard, and Prediction Guard, giving you the specifics needed to make a defensible procurement decision.

Comparing governance architectures for regulated AI

The most important question to ask any AI governance vendor is not "what standards do you cover?" It is "where does your enforcement actually run, and where does your telemetry actually go?" For regulated enterprises where manufacturing IP, controlled unclassified information, or non-public personal financial data is in scope, those two questions determine whether a platform can be deployed at all.

The three platforms occupy three distinct architectural positions:

• Noma Security operates as an AI security posture management tool providing runtime monitoring, threat detection, and policy enforcement across the AI application, orchestration, and model stack. Its Kong Gateway plugin, which enables inline request interception, requires outbound HTTPS to api.noma.security on port 443 in its default configuration (see Kong Gateway plugin documentation). AI governance policy evaluation for requests transiting that plugin routes to Noma's SaaS infrastructure.
• Lakera Guard is a content moderation and prompt security platform offering real-time detection of prompt injection, jailbreaks, toxic content, PII leakage, and other AI-specific threats. It operates in two architectural modes. In its direct API mode, it evaluates AI inputs (prompts, retrieved documents, and tool calls) and returns a response to the calling application, with blocking logic implemented at the application level. In its Kong AI Lakera Guard plugin mode, the plugin enforces blocking inline via Kong Gateway, with the enforcement decision computed in Lakera's cloud service (api.lakera.ai/v2/guard). In both modes, AI interaction content is routed to Lakera's SaaS infrastructure for evaluation, making the architectural sovereignty question not whether enforcement is inline, but where the evaluation is computed.
• Prediction Guard deploys the entire control plane inside your infrastructure (on-premises, cloud VPC, or air-gapped) with no external SaaS dependencies. Runtime policy enforcement happens at the data plane. Every model or agent call is checked against governance policy before the call completes, and the audit log is generated inside your environment, then consumed by your own SIEM.

Comparing AI risk and compliance features

The critical distinction in risk and compliance tooling is whether a platform generates risk and compliance evidence or actively enforces the controls that governance frameworks require. Lakera Guard provides real-time threat detection across both its direct API and Kong Gateway plugin modes. In either mode, enforcement decisions are computed in Lakera's SaaS (api.lakera.ai). The architectural distinction is not whether enforcement is inline (the Kong plugin enforces blocking inline). It is whether you control where the evaluation logic runs and where AI interaction data is processed.

Noma provides centralized AI threat detection, compliance validation, and anomaly detection across AI system layers. Its runtime security capabilities are real, and its published documentation explicitly describes runtime policy enforcement and real-time monitoring. The architectural question is not whether Noma enforces policies: it is whether you control where that enforcement logic runs and where the telemetry flows when using the Kong Gateway integration in its cloud-connected configuration.

How Lakera Guard supports risk and compliance

Lakera Guard's risk and compliance capabilities address specific threat categories within the NIST AI Risk Management Framework. Prompt injection detection, jailbreak prevention, PII leakage detection, and toxic content filtering collectively address aspects of the Map and Measure functions by identifying AI-specific risks in real time.

What matters architecturally is where enforcement decisions are computed. In Lakera Guard's direct API mode, the application team writes the blocking logic, and two teams can produce different enforcement outcomes depending on how each implements that logic. In its Kong AI Lakera Guard plugin mode, blocking is enforced inline by the plugin, removing that application-level variance. In both modes, however, AI interaction content is routed to Lakera's cloud service (api.lakera.ai) for evaluation. For regulated enterprises where no AI interaction metadata can transit a third-party SaaS endpoint, that SaaS evaluation dependency is the controlling architectural characteristic.

Mapping platforms to risk requirements

For a CISO in financial services preparing for an OCC examiner review, or a risk and compliance leader in a defense-adjacent organization preparing for a DCSA assessor visit, the distinction between detective controls and preventive controls is not academic. Preventive controls block a non-compliant action before it completes. Detective controls log and alert after the fact. Both are required by governance frameworks, but for high-risk data flows involving controlled unclassified information or non-public personal financial data, passive alerting after a data exposure event documents the failure rather than preventing it.

The question for your next audit cycle is whether you can demonstrate that violations were blocked before they occurred, not merely logged after the fact. The scaling agentic AI governance analysis covers this distinction in the context of enterprise agent deployments at scale.

Real-time observability and audit readiness

For regulated enterprises, real-time does not mean low-latency dashboards. It means evidence that a control operated at the moment a transaction occurred. The platform's deployment architecture determines whether that evidence is generated inside your perimeter or reconstructed from a vendor-managed environment after the fact.

The term "real-time" means something specific to an FFIEC examiner or Office for Civil Rights (OCR) investigator: evidence that a control operated at the moment a transaction occurred, not evidence assembled after the fact from scattered log fragments.

Enforcement and evidence inside your perimeter

Noma provides real-time visibility across the AI application, orchestration, and agentic stack. That visibility is genuine. For environments where the Kong Gateway plugin operates in its default cloud-connected configuration, the monitoring infrastructure requires outbound connectivity to Noma's cloud. For enterprises under ITAR or handling controlled unclassified information, routing AI interaction metadata to a third-party SaaS endpoint triggers vendor management obligations that many regulated organizations cannot satisfy within their existing third-party risk programs without a formal risk acceptance process.

Lakera Guard routes AI interaction content to its cloud service (api.lakera.ai) for evaluation in both its direct API and Kong AI Lakera Guard plugin modes. This SaaS evaluation dependency means AI interaction metadata transits Lakera's infrastructure regardless of integration mode. Regulated enterprises operating under ITAR or handling controlled unclassified information should assess this dependency against their third-party risk programs before architectural commitment.

Prediction Guard's runtime enforcement runs entirely inside your environment. The control plane checks each model or agent call against governance policy before the call completes, allowing it, blocking it, or rewriting it. The audit log generated by that enforcement event stays inside your perimeter and forwards natively to Splunk, Datadog, or a generic syslog endpoint. No AI interaction metadata transits Prediction Guard's infrastructure. The Practical AI episode 360, co-hosted by Prediction Guard CEO Daniel Whitenack with Chris Benson, covers how zero-trust principles map to runtime enforcement in production agent deployments.

Evidence collection for a structured audit requires three things: proof that a control was configured, proof that the control operated on the relevant transactions, and proof that the evidence was generated inside a trusted environment you control. Prediction Guard generates all three inside your environment for every deployment. Because the control plane enforces policy at the moment of each call, the audit log is the structured record of enforcement that happened, not a retrospective score applied to a sample. Prediction Guard doesn't hold SIEM credentials or store log data: it formats the output and your ingestion pipeline handles delivery.

Scaling governance without manual overload

AI deployment outpaces the governance processes built to track it, and the gap between what runs in production and what appears in the official inventory is where unquantified risk accumulates. How each platform closes that gap, through structural system-level enforcement or through policies delegated to application teams, defines its real governance capacity.

The governance capacity problem is real. Engineering teams deploy AI agents and integrations faster than security processes capture them, and the gap between what runs in production and what appears in the official inventory represents unquantified risk exposure.

Hard enforcement vs. advisory policies

Advisory AI governance policies documented in a wiki depend on engineers choosing to follow them under delivery pressure. System-level enforcement does not. This distinction is the operational heart of the comparison between Lakera Guard, Noma, and Prediction Guard.

In Lakera Guard's direct API mode, blocking logic is written and maintained by the application team, and AI governance policies managed at the application level accumulate control drift between audit cycles. In its Kong AI Lakera Guard plugin mode, blocking is enforced inline by the plugin independently of application code. In both modes, enforcement decisions are computed in Lakera's cloud service (api.lakera.ai), so the governance dependency that remains constant across modes is not application-layer discipline but third-party SaaS reachability and data processing.

Prediction Guard's separation of duties design removes enforcement responsibility from the application layer entirely. GRC teams configure policies on the Govern page of the Admin Console. Developers point existing OpenAI-compatible or Anthropic-compatible SDK calls at the Prediction Guard endpoint by changing only the base_url. The control plane enforces configured policies on every call, regardless of which engineer wrote the application code or which agent framework they used.

Preventing ungoverned agent data exposure

When engineering teams route production data through external AI API endpoints, that data leaves your perimeter without an audit record you control. For manufacturing environments with proprietary process data or financial services teams handling non-public personal financial information under GLBA, ungoverned agent interactions represent the highest-risk exposure vector under AIUC-1's Data and Privacy pillar.

Prediction Guard's self-hosted deployment eliminates this exposure class. The model call, the AI governance policy check, and the audit record all occur inside your environment. There is no API call to an external endpoint that could serve as a data exfiltration pathway.

Auditing against AIUC-1 and supporting frameworks

Risk and compliance coverage claims are only as durable as the underlying enforcement architecture that generates the evidence. The sections below map each platform's structural enforcement capabilities against AIUC-1, the EU AI Act, OWASP, and ISO 42001 to show where genuine mitigation ends and documentation begins.

Audit readiness for AIUC-1 metrics

AIUC-1 organizes AI governance requirements across six pillars: Data and Privacy, Security, Safety, Reliability, Accountability, and Society. Enterprise procurement teams are including AIUC-1 conformity attestation in AI vendor selection processes, making it the practical cross-framework anchor for regulated enterprises evaluating AI governance infrastructure across multiple regulatory obligations simultaneously. The Practical AI episode 361 covers how AIUC-1 crosswalks a single control implementation across the frameworks regulated procurement teams are measured against.

AIUC-1 organizes AI governance requirements across six pillars: Data and Privacy, Security, Safety, Reliability, Accountability, and Society. Enterprise procurement teams are including AIUC-1 conformity attestation in AI vendor selection processes, making it the practical cross-framework anchor for regulated enterprises evaluating AI governance infrastructure across multiple regulatory obligations simultaneously. The Practical AI episode 361 covers AIUC-1 and how enterprise AI governance frameworks apply to regulated procurement decisions.

Mapping EU AI Act obligations

The EU AI Act mandates continuous monitoring, transparency, and risk management for AI systems categorized as high-risk. Lakera Guard's threat detection capabilities generate evidence of identified risks, supporting transparency and monitoring requirements. In its Kong AI Lakera Guard plugin mode, the plugin enforces blocking inline, addressing the Act's mitigation requirement at the integration layer rather than delegating it to application code. In both modes, enforcement decisions are computed in Lakera's cloud service (api.lakera.ai), so organizations assessing EU AI Act data processing obligations should evaluate that SaaS dependency against applicable transfer and processing requirements.

For organizations using Noma's Kong Gateway plugin in its cloud-connected configuration, routing AI interaction metadata to an external endpoint requires assessment against applicable data processing agreements before deployment.

Mitigating OWASP threats

The OWASP Top 10 for Agentic Applications 2026 and the OWASP LLM Top Ten define the community guidance standard for AI security practitioners. Prompt injection is the most consistently cited risk across regulated-industry deployments.

In the Kong Gateway cloud-connected configuration, AI inputs (prompts, retrieved documents, and tool calls) transit Noma's SaaS infrastructure as part of the detection process. Prediction Guard's prompt injection detection runs inside your environment, the check happens before the model call completes, and no prompt content transits external infrastructure.

ISO 42001 audit evidence mapping

ISO/IEC 42001 requires a documented AI management system with continuous monitoring, risk assessment, and evidence of control effectiveness. Conformity assessors look for structured evidence that controls operated on actual production transactions, not sampled retrospective reviews. AIUC-1's Accountability pillar maps directly to this requirement, making them complementary anchors for organizations pursuing formal certification.

Prediction Guard's per-call enforcement log, consumed by your SIEM, produces the structured evidence trail that certification body assessors require. Each enforcement event is timestamped, records the policy applied, and documents the outcome. For a team relying on Lakera Guard's cloud-evaluated verdicts or Noma's cloud-hosted audit log, producing the same evidence package requires extracting logs from a vendor-managed environment or accepting that the evaluation occurred outside your perimeter. The NIST AI RMF implementation playbook covers the evidence collection requirements that OCC, DCSA, and AIUC-1 assessors examine function by function.

Evaluating deployment models for data sovereignty

Where enforcement logic runs and where telemetry flows are the two architectural questions that determine whether a platform can be deployed in a regulated environment at all. The comparison below examines each platform across the deployment dimensions that matter most to procurement teams operating under data residency obligations.

Deployment options for data sovereignty

Noma's Kong Gateway architecture, in its default cloud-connected configuration, creates a sovereignty constraint for organizations where no AI interaction metadata can transit third-party infrastructure. Regulated enterprises operating under CMMC or ITAR should confirm their specific deployment requirements with Noma before evaluation.

Lakera Guard routes AI interaction content to api.lakera.ai for evaluation in both deployment modes. Regulated enterprises should confirm data processing and residency terms with Lakera before architectural commitment.

Integrating AI logs with enterprise SIEM

Prediction Guard's SIEM integration configures how audit log output is formatted to match the field structure that each SIEM expects natively. The integration process works through the Monitor page of the Admin Console:

1. Open the Monitor page and select the target integration (Splunk, Datadog, CrowdStrike, or generic syslog).
2. Click Configure under the chosen integration.
3. Confirm to make the integration live.
4. The control plane formats audit log output using the field structure the SIEM expects natively.

Prediction Guard doesn't hold SIEM API keys, HTTP Event Collector (HEC) tokens, or endpoint credentials. Your existing ingestion pipeline handles delivery. The entire evidence chain from enforcement event to SIEM record stays inside your infrastructure and under your access controls.

Tailoring AI governance for regulated sectors

Regulatory obligations vary by sector, but the underlying architectural requirement is consistent: enforcement must be structural, evidence must be generated inside a trusted environment, and audit records must reflect controls that operated on actual production transactions. The sections below cover the sector-specific considerations for financial services and defense-adjacent environments.

Risk and compliance for banking AI deployments

FFIEC examiners and OCC reviewers focus on whether AI applications processing non-public personal financial information have governance controls that are structural rather than advisory. GLBA requires financial institutions to protect customer financial data from unauthorized access or use. When AI agents process customer financial records, the governance question is whether that policy is enforced on every AI interaction, not whether an AI governance policy document exists.

Financial services organizations deploying Lakera Guard's direct API mode depend on enforcement logic written and maintained by engineering teams at the application level. In its Kong AI Lakera Guard plugin mode, blocking is enforced inline by the plugin. In both modes, enforcement decisions are computed in Lakera's cloud service (api.lakera.ai). For an OCC examiner whose question is whether non-public personal financial information transited an unauthorized third-party SaaS endpoint, the routing dependency is the governing architectural question, not whether enforcement was inline.

Securing AI in defense-adjacent environments

DCSA assessors evaluating CMMC controls and organizations managing ITAR obligations operate under explicit prohibitions on routing controlled technical data through unauthorized external networks. For these organizations, any outbound telemetry dependency in a default Kong Gateway configuration is a disqualifying architectural characteristic that requires a formal risk acceptance process most security programs will not approve.

Prediction Guard's air-gapped deployment option addresses these requirements directly. The control plane, governance logic, and audit logs operate entirely inside your secure enclave. No network connectivity to external infrastructure is required after initial deployment.

Which platform fits your governance requirement?

If your primary requirement is real-time content moderation, prompt injection detection, and jailbreak prevention, Lakera Guard addresses those requirements in both its direct API and Kong AI Lakera Guard plugin modes. The architectural constraint common to both modes is that enforcement decisions are computed in Lakera's cloud service (api.lakera.ai). For regulated environments where AI interaction metadata cannot transit a third-party SaaS endpoint, that dependency is disqualifying regardless of which integration mode is selected.

If your environment can operate with third-party SaaS connectivity in your chosen deployment mode and your primary concern is AI security posture management and threat detection, Noma Security offers meaningful capability. Verify exactly which enforcement capabilities are available in each deployment mode and confirm that your chosen configuration is compatible with your regulatory obligations before committing.

If your environment requires active runtime enforcement of AIUC-1, NIST AI RMF, OWASP, and EU AI Act policies before model calls complete, with no telemetry leaving your perimeter and audit logs generated inside your own SIEM pipeline, Prediction Guard provides the architecture that satisfies those requirements. The product comparisons page covers additional head-to-head architectural comparisons, and the developer integration overview covers how OpenAI and Anthropic API compatibility works in practice.

Book a deployment scoping call at predictionguard.com/get-started to assess how the self-hosted control plane fits your infrastructure, your existing SIEM stack, and your regulatory obligations.

FAQs

Does Noma Security support air-gapped deployments?

Noma's Kong Gateway plugin requires outbound HTTPS to api.noma.security on port 443 in its default cloud-connected configuration. No on-premises or air-gapped deployment option has been confirmed by Noma's published documentation; verify current deployment options directly with Noma before proceeding with an air-gapped evaluation.

Is Lakera Guard a real-time enforcement platform or a detection tool?

Lakera Guard operates in two modes. In its direct API mode, it returns a response to the calling application, with blocking logic implemented by the application team. In its Kong AI Lakera Guard plugin mode, blocking is enforced inline via Kong Gateway. In both modes, enforcement decisions are computed in Lakera's cloud service (api.lakera.ai). The relevant question for regulated enterprises is not whether enforcement is inline, but whether AI interaction content can transit Lakera's SaaS infrastructure under their regulatory obligations.

What does "self-hosted" mean for Prediction Guard's deployment?

Prediction Guard deploys the entire control plane inside your own infrastructure: on-premises, cloud VPC, or air-gapped. No AI interaction data transits Prediction Guard's servers, and audit logs are generated inside your environment and consumed by your own SIEM.

How does Prediction Guard format audit logs for Splunk or Datadog?

The Monitor page configures audit log output formatting to match each SIEM's native field structure. Your existing ingestion pipeline handles delivery without Prediction Guard holding any SIEM credentials.

Which frameworks does Prediction Guard enforce at runtime?

Prediction Guard enforces AIUC-1 policies mapped to NIST AI RMF, OWASP Top 10 for Agentic Applications 2026, EU AI Act, and ISO/IEC 42001 requirements at the control plane level. Every model or agent call is checked against configured policies before the call completes.

Can developers use their existing OpenAI SDK code with Prediction Guard?

Yes. Developers change only the base_url in their existing OpenAI-compatible or Anthropic-compatible SDK calls. The control plane enforces governance policies transparently without requiring application code changes.

Key terms glossary

Sovereign AI control plane: A self-hosted governance infrastructure that governs all AI models, tools, and agentic workflows under a single, policy-enforced API, enforcing AI policy at runtime entirely inside your own environment with no external data dependencies.

Preventive control: A security control that blocks a non-compliant action before it completes, as distinct from a detective control that alerts after an event has occurred.

AIUC-1: A cross-framework AI governance standard organized across six pillars (Data and Privacy, Security, Safety, Reliability, Accountability, Society) used by enterprise procurement teams for structured vendor due diligence.

Outbound telemetry dependency: An architectural characteristic where a governance platform routes enforcement decisions or monitoring data to an external SaaS endpoint, creating a data sovereignty constraint for regulated enterprises.

NIST AI RMF MANAGE function: The NIST AI Risk Management Framework function covering runtime response to measured risk, including plans to respond to, recover from, and communicate about AI incidents on a per-transaction basis.