Noma Security alternatives for regulated industries: 2026 enterprise AI governance evaluation

Updated July 21, 2026

TL;DR: Regulated enterprises evaluating Noma Security alternatives in 2026 need to resolve a foundational architecture question before any feature comparison: where does policy enforcement actually execute, and where does telemetry go? Noma's Kong Gateway plugin routes data outside your perimeter via outbound HTTPS to api.noma.security:443. Prediction Guard deploys the entire control plane self-hosted inside your own infrastructure, enforcing policies on every agent call before the response returns. For organizations handling CUI, non-public personal financial information, or PHI, that routing difference determines whether your next regulatory review is defensible or not.

Federal Financial Institutions Examination Council (FFIEC) examiners, Cybersecurity Maturity Model Certification (CMMC) assessors, and AIUC-1 assessors ask a single question that exposes most AI governance programs: where are your AI agent's telemetry and policy enforcement logs stored? If the answer is "in a third-party vendor's external infrastructure," you are looking at an audit finding before the examiner asks a second question.

For security and compliance leaders in manufacturing, financial services, defense-adjacent operations, and healthcare, evaluating AI governance platforms in 2026 is not about checking a feature list. It is about verifying where your data actually flows. This evaluation compares Noma Security with self-hosted alternatives including Prediction Guard, Fiddler AI, and Vertex AI across deployment architecture, regulatory alignment, and sector-specific requirements so you can build a defensible case for your board, your legal team, or your next regulatory examination.

Key metrics for enterprise AI governance

Before comparing vendors, you need a clear definition of what a governed AI deployment must demonstrate to survive a structured review. Three metrics carry the most weight in 2026 evaluations:

• Runtime enforcement coverage: Does the platform evaluate and enforce policy on every agent call as it happens, blocking or rewriting inputs and outputs before the model call completes, or does it operate asynchronously, logging after the fact without the ability to intervene?
• Audit log localization: Are enforcement records generated and retained inside your infrastructure, or does the evidence trail live in a vendor-controlled environment outside your perimeter?
• Framework alignment depth: Does the vendor publish a structured, itemized capability-to-framework mapping table that a third-party conformity assessor can validate line by line? Broad compliance claims without that mapping are not defensible in a structured review.

Mapping governance to NIST

The NIST AI RMF core organizes AI risk management across four functions: Govern, Map, Measure, and Manage. Sovereign AI control planes enforce governance differently than SaaS-based posture management tools across each of those functions. The OWASP Top 10 for Agentic Applications 2026 identifies ten failure modes that each represent a runtime enforcement surface, not a configuration setting.

Control-to-framework crosswalk

For regulated enterprises governing agentic deployments, testing methodology that proves a model's behavior is consistent and explainable becomes the foundation when your audit depends on demonstrating control rather than asserting it.

Tailoring governance for regulated sectors

Generic AI security marketing fails in regulated industries because each sector operates under distinct examiner expectations. Defense-adjacent organizations handling Controlled Unclassified Information face CMMC and ITAR constraints that prohibit exporting that data to external networks. Financial institutions under GLBA and FFIEC oversight must demonstrate that non-public personal financial information does not transit unauthorized third-party infrastructure. Healthcare organizations governed by HIPAA must prove that PHI remains within a defined and auditable trust boundary.

AIUC-1, a cross-framework standard organized across six pillars (Data and Privacy, Security, Safety, Reliability, Accountability, and Society), is emerging as a key enterprise procurement requirement. The Practical AI episode 361 covers how the enterprise flywheel of standards, certification, audit, and insurance is being applied to AI agents. Insurers are beginning to explore how AI coverage terms could create market-based incentives for organizations to adhere to government or third-party safety requirements as a condition of coverage. A vendor that routes your telemetry externally cannot satisfy the Data and Privacy pillar for most regulated workloads.

Comparing AI governance architectures

Self-hosted vs. SaaS deployment trade-offs

Governance gap checklist: scoring your current audit readiness

Use the six AIUC-1 pillars as a structured checklist before entering any vendor evaluation:

1. Data and Privacy: Can you demonstrate today that no AI interaction has transited an unauthorized external network?
2. Security: Is every agent call evaluated against a prompt injection defense before the model processes it?
3. Safety: Is there an active mechanism to block harmful or non-compliant outputs before they reach downstream systems or users?
4. Reliability: Is factual consistency checking applied to model outputs before delivery?
5. Accountability: Are structured audit logs generated inside your perimeter and available in your SIEM for every enforcement
6. decision, and is there a registered AI asset inventory that your risk committee can review and your AIUC-1 assessor can validate?

• Society: Are there controls in place to detect and prevent your AI systems from being used to enable cyberattacks, generate CBRN-relevant content, or produce other outputs that create broad societal harm? If your answer to any of these is "not yet" or "we rely on developer guidelines," you have a system-level control gap, not a documentation gap.

Evaluating Noma Security for enterprise AI risk

Noma Security offers AI security posture management with integrations for Copilot Studio and Salesforce AgentForce that give visibility into AI asset usage across multi-vendor environments. For organizations without strict data sovereignty requirements, those discovery and monitoring capabilities represent real value, particularly when AI usage is distributed across business units faster than governance processes capture it.

Deployment patterns for regulated AI

The technical constraint that creates the compliance gap in regulated environments comes directly from Noma's Kong AI Gateway plugin. Per the Kong developer documentation, the plugin integrates Kong traffic flows with Noma Security Cloud Services for centralized security oversight, streaming AI traffic data to the Noma Security Console for comprehensive audit trails and behavioral analysis. The plugin configuration specifies the API base as https://api.noma.security, requiring outbound HTTPS access to that external endpoint on port 443. The Kong-level integration itself does not require developer code changes, but the network-level routing to Noma's external infrastructure is fixed.

The plugin operates in two modes. In monitor mode, the AI-DR component runs asynchronously and logs requests without blocking them, adding no latency. In blocking mode, AI-DR runs synchronously and requests may be rejected based on the external verdict returned from Noma's cloud infrastructure. The enforcement decision in blocking mode depends on a live connection to Noma's external systems.

Regulatory compliance gaps to watch

For organizations handling CUI, this architecture creates a gap that CMMC assessors and DCSA assessors address directly. CMMC categorizes assets by whether they store, transmit, or process regulated data, and an outbound telemetry stream carrying AI interaction data to an external API may constitute unauthorized transmission of CUI if that data is present in the prompts or outputs being analyzed. The FFIEC Information Security Booklet requires financial institutions to control the electronic transmission of sensitive information and monitor for unauthorized disclosure of customer information across networks and cloud environments. An AI governance tool routing telemetry containing non-public personal financial information to an external cloud endpoint introduces a third-party data pathway that FFIEC examiners and OCC examiners will scrutinize. The Practical AI episode 360, co-hosted by Prediction Guard founder Daniel Whitenack, covers why vendor-mediated telemetry crossings break the trust boundary that regulated data depends on.

Key alternatives for scalable AI compliance

NIST AI RMF compliance capabilities: Prediction Guard

Prediction Guard deploys a sovereign AI control plane inside the customer's own infrastructure, whether self-hosted, in a cloud VPC, or air-gapped. Every model or agent call passes through the control plane, which evaluates it against configured governance policies and either allows, blocks, or rewrites the interaction before the response returns. Audit logs are generated inside your environment as the byproduct of that enforcement, then consumed by your existing SIEM.

The separation of duties model is central to enterprise governance. Developers point their existing OpenAI-compatible or Anthropic-compatible SDK calls at the control plane endpoint by changing only the base_url, as described in the Prediction Guard product overview. No code rewrites. Security and GRC teams configure governance policies on the Govern page of the Admin Console. The control plane enforces those policies on every request regardless of which SDK the developer chose. Prediction Guard does not hold SIEM credentials, API keys, or HEC tokens. It configures output formatting so your existing ingestion pipeline handles delivery under your own controls, as detailed in the real-time monitoring guide. The NIST AI RMF implementation playbook maps specific capabilities to each of the four NIST AI RMF functions with enough detail to support structured audit preparation.

Fiddler AI: mapping to NIST standards

Fiddler AI positions itself as an "AI Control Plane" delivering monitoring, guardrails, and governance for compound AI systems, with Responsible AI features supporting bias mitigation and explainability. For environments where production observability and model behavior tracking are the primary drivers, the platform is a strong fit. For regulated enterprises whose evaluation is anchored on where enforcement executes and where audit logs are retained, Fiddler's deployment options should be verified against the same self-hosted, air-gapped, and SIEM-local-retention criteria applied to any governance vendor.

Vertex AI / Gemini Enterprise Agent Platform: risk and control mapping

Google Cloud's Vertex AI (now branded as the Gemini Enterprise Agent Platform) orchestrates multiple models within a governed cloud environment, but its governance capabilities remain GCP-native. VPC Service Controls create a perimeter around GCP resources, and organizations with multi-cloud or multi-model requirements should verify how governance enforcement applies across their full model estate before committing to a GCP-native control architecture, as an enterprise architecture analysis from Info Services notes. Governance configuration is tied to Google's infrastructure, and switching providers requires rebuilding your control plane from scratch. AWS Bedrock Guardrails and Azure AI Content Safety face an equivalent structural constraint, functioning as point-solution filters that deepen dependence on a single hyperscaler rather than providing a portable, sovereign control plane.

Critical capabilities for AI audit readiness

Runtime enforcement for AI workflows

Runtime enforcement separates a governance control plane from a monitoring tool. A monitoring tool records what happened. A control plane decides what is allowed to happen, in real time, before the interaction completes. For regulated enterprises, that distinction is not academic: a logged violation that reached the model, the downstream system, or the end user is a compliance event, while a blocked violation is not. The agentic AI governance blog post describes how the Prediction Guard control plane enforces NIST AI RMF and OWASP Agentic AI policies at runtime, blocking non-compliant requests before they reach the model or downstream system. That enforcement happens at the moment of the call. The audit log records that enforcement happened, which is not the same as the enforcement mechanism itself.

Structuring logs for SIEM readiness

The Prediction Guard Monitor page configures SIEM integration in four steps: open the Monitor page in the Admin Console, click Configure under the chosen integration (Splunk, Datadog, CrowdStrike, or generic syslog), confirm to make the integration live, and the live integration signals Prediction Guard to format audit log output using the field structure that SIEM expects natively. Your existing SIEM ingestion pipeline handles delivery. Prediction Guard does not enter, store, or test any SIEM credentials.

Verifying AI model provenance

AI System registration in Prediction Guard captures models, MCP servers, datasets, and dependencies within a structured inventory. The exportable artifact is an AIBOM in CycloneDX format, giving compliance teams a structured inventory they can hand to an AIUC-1 assessor or an enterprise procurement reviewer. The AIBOM answers the asset question: which models are in production, on which data, under which policies. It does not replace per-model risk assessment. An AIBOM without per-model evaluation tells you which models are in production but not whether they are exploitable under adversarial conditions. Per-model evaluation without an AIBOM tells you which vulnerabilities exist but not which production models carry them. Regulated enterprises need both.

Sector-specific deployment considerations

Defense-adjacent and federal

For organizations handling CUI under CMMC, the deployment architecture is non-negotiable. CUI must be stored, processed, and transmitted only within authorized environments. An AI governance tool that routes telemetry to an external cloud endpoint, even encrypted, must be explicitly authorized for that CUI category or it creates a transmission exposure that a CMMC C3PAO organization will flag. Air-gapped deployment eliminates this risk rather than managing it. Noblis, a defense-adjacent technology organization and Prediction Guard investor, is collaborating through their Artificial Intelligence Assurance Implementation (AI2) program to support secure AI deployment and help customers safeguard sensitive data across defense-adjacent workloads.

Healthcare and clinical environments

Healthcare organizations deploying AI for clinical summarization, patient routing, or field care decision support face a clear constraint: PHI cannot be transmitted to an external API for policy evaluation. The enforcement decision must happen locally, before any data moves. The SimWerx medic copilot (an established deployment for military, EMS, and disaster relief field medics) demonstrates this requirement in practice. In pre-hospital environments, AI decision support must function without any external network dependency because the enforcement decision cannot wait for a cloud connection that may not be available in the field. That operational reality rules out any architecture where enforcement depends on an external cloud verdict.

Financial services

Financial AI vendor capability matrix

For FFIEC examiners and OCC examiners reviewing AI governance programs, the key evidence package includes a complete AI asset inventory (AIBOM), structured audit logs demonstrating enforcement happened on every interaction, and a capability-to-framework mapping document. Automated evidence collection is the only approach that keeps pace with the rate of AI deployment in production financial services environments. Manual spreadsheets become stale between deployment and examination.

Manufacturing and AI supply chain risk

Manufacturing environments face dual exposure: proprietary IP that cannot leave the perimeter, and operational technology systems that agents may interact with directly. AI supply chain vulnerability scanning addresses the risk that a compromised model or tool introduces a control deficiency into the governed system before detection. Organizations in aerospace and aviation technology have deployed self-hosted AI governance for field operations using engine data, demonstrating manufacturing-adjacent deployments where proprietary operational data cannot transit third-party infrastructure. The scaling agentic AI article details the governance trade-offs that arise when agents interact with operational systems at enterprise scale.

Key considerations for regulated AI deployments

Securing autonomous agent workflows

Agentic AI introduces governance complexity that single-model evaluations do not capture. When an agent calls external tools, retrieves from knowledge bases, and passes outputs to downstream systems, each handshake is a potential enforcement surface. The OWASP Top 10 for Agentic Applications 2026 documents ten failure modes, from agent goal hijack through rogue agents, that emerge specifically from agentic system architectures. A governance control plane that enforces policy only at the initial prompt layer may leave critical agentic vulnerabilities unaddressed, including tool misuse, identity and privilege abuse, and insecure inter-agent communication. MCP tools registered directly within the AI System configuration in Prediction Guard produce an inventory of authorized tools for that agent, supporting governance policy definition and audit review. The Practical AI episode 358 covers how enterprise Kubernetes stacks are being restructured to support governed AI systems with MCP integrations inside the customer's own infrastructure.

Evaluating vendors on evidence quality

Structure every vendor evaluation around evidence quality rather than capability claims. Three categories of evidence carry weight with a structured reviewer:

• A structured, itemized capability-to-framework mapping document covering NIST AI RMF functions, OWASP Top 10 for Agentic Applications categories, AIUC-1 pillars, or EU AI Act articles, depending on your primary regulatory exposure
• Technical documentation of where enforcement decisions execute and where telemetry routes, with specific API endpoint or network documentation
• A named peer reference in your industry sector who has completed deployment and survived at least one internal or external audit review

To verify AIUC-1 support, ask for the policy-to-pillar mapping document and verify it against the crosswalk published at aiuc-1.com/crosswalks. A vendor that cannot produce this mapping as a structured, itemized document is providing a compliance assertion, not compliance evidence. Read the vendor's data processing addendum before any technical evaluation. Confirm whether the vendor's definition of "processing" includes prompt telemetry, output telemetry, or metadata, and confirm where audit logs and enforcement records are stored, who controls access to them, and how you retrieve them if the vendor relationship ends.

Maintaining audit trails during migration

When you swap models or update agent workflows, enforcement continuity is the audit readiness question. Because the Prediction Guard control plane operates at the API level rather than the model level, changing the underlying model or adding a new tool to an agent workflow does not interrupt the audit log stream. The control plane continues evaluating every call against configured governance policies regardless of what model or tool sits behind it. That model-agnostic architecture is what makes governance portable across vendors rather than locked to a specific model family or provider.

If your current AI governance approach depends on developers following documented guidelines, enforcement happens at the discretion of individual developers rather than at the system level, which creates gaps in your audit trail. Book a deployment scoping call to evaluate whether self-hosted deployment fits your compliance requirements, or review the NIST AI RMF implementation playbook to assess which framework functions Prediction Guard addresses at the system level before that conversation.

FAQs

Does Prediction Guard store your audit logs?

No. Prediction Guard generates structured, SIEM-ready audit logs inside your environment as a byproduct of runtime enforcement, but storage, retention, and search are handled entirely by your existing SIEM. Prediction Guard does not hold your logs, your SIEM credentials, or any API keys.

How does Noma Security's architecture differ from Prediction Guard's?

Noma Security's Kong Gateway plugin routes telemetry and policy enforcement data to api.noma.security:443 outside your perimeter, per the Kong developer documentation. Prediction Guard runs the entire control plane self-hosted inside your infrastructure, so governance logic, policy enforcement, and audit logs never transit vendor systems.

Does implementing Prediction Guard require developers to rewrite their code?

No. Prediction Guard is fully compatible with OpenAI and Anthropic APIs. Developers change only the base_url in their existing SDK calls, and governance is enforced transparently by the control plane on every request without any changes to application code.

What frameworks does Prediction Guard align with out of the box?

Prediction Guard provides built-in policy mapping for the NIST AI RMF, NIST AI 600-1, OWASP Top 10 for Agentic Applications, AIUC-1, and the EU AI Act, with published capability-to-framework mapping tables for each.

Is Noma Security self-hosted?

No. Based on available documentation, Noma Security's enforcement architecture routes telemetry to external cloud infrastructure at api.noma.security. Organizations with strict data sovereignty requirements under CMMC, ITAR, GLBA, or HIPAA should evaluate whether this architecture is compatible with their data handling obligations before deployment.

What is the difference between an AI firewall and a sovereign AI control plane?

An AI firewall filters inputs and outputs at the perimeter of an AI application. A sovereign AI control plane governs the entire AI system: model and tool lifecycle, identity and access for agent calls, factual consistency checking, AI asset registration with AIBOM export, and framework-mapped policy enforcement across multi-vendor model ecosystems. Firewalls handle I/O filtering. Control planes govern the full composition of the AI system.

Key terms glossary

Sovereign AI control plane: A self-hosted software system that unifies, secures, and governs disparate AI models, tools, and applications entirely within an organization's secure perimeter, enforcing policies at the system level on every agent call.

Runtime policy enforcement: The active process of evaluating, allowing, blocking, or rewriting AI inputs and outputs in real time before a model call completes, as distinct from retrospective log analysis.

Factual consistency: A probabilistic evaluation of whether an AI system's output is grounded in and consistent with the provided reference documents or knowledge base.

AIBOM (AI Bill of Materials): A structured inventory, exported in CycloneDX format, detailing all models, datasets, tools, and dependencies utilized within an AI system, produced as a byproduct of AI System registration.

AIUC-1: A cross-framework standard structured around six pillars (Data and Privacy, Security, Safety, Reliability, Accountability, and Society) used to assess AI system conformity and increasingly required in enterprise vendor due diligence.

Agentic AI exposure: The risk profile that emerges when autonomous AI agents make tool calls, retrieve from external knowledge bases, and pass outputs to downstream systems without centralized governance visibility or control.

FFIEC (Federal Financial Institutions Examination Council): An interagency body that prescribes uniform principles, standards, and report forms for federal examination of financial institutions and makes recommendations to promote uniformity in supervision.

CMMC (Cybersecurity Maturity Model Certification): A unified standard for implementing cybersecurity across the defense industrial base, required for organizations handling Controlled Unclassified Information (CUI).

GLBA (Gramm-Leach-Bliley Act): Federal law requiring financial institutions to explain their information-sharing practices and safeguard sensitive data.

ITAR (International Traffic in Arms Regulations): U.S. regulatory regime to restrict and control the export of defense and military-related technologies.

SIEM (Security Information and Event Management): Technology that provides real-time analysis of security alerts generated by applications and network hardware.

MCP (Model Context Protocol): A protocol for registering tools that AI agents can call during execution, producing an inventory that supports governance policy definition and audit review.