Your AI is in production. Its compliance isn't. Govern every AI with trust.
AI creates obligations that annual, manual compliance was never designed to meet. Most GRC programs were built for systems that change on a release cycle measured in months. AI does not work that way. Models retrain, agents pick up new tools, and retrieval pipelines pull in new data, sometimes in the same week a control was last checked. The result is a governance model that is structurally out of date the moment it is finished.
The EU AI Act, ISO/IEC 42001 and a growing list of sector rules have turned AI governance into a binding, audited obligation. Enterprise AI adoption can move faster than compliance processes, so waiting for a mature process to form on its own creates a growing governance and evidence gap.
Models, RAG pipelines and agentic AI applications can sit outside the controls your existing framework was built around. They may not appear in asset registers, risk taxonomies or control catalogs designed for traditional IT systems, so they are governed informally or not at all. Generative AI deployments, autonomous agents and third-party AI integrations also introduce risk that must be tied back to the enterprise governance model.
A point-in-time assessment shows how a system behaved on the day it was tested. It does not show the model retrained last week, the changed retrieval source or the agent that gained a new permission yesterday. Without continuous AI audit evidence, every audit risks starting from a blank page, and it becomes harder to demonstrate how sensitive data, AI outputs, misuse and tool misuse were governed across the lifecycle.
No named owner, no control mapped to that owner, and no board-level view of where AI risk actually sits means no single answer is ready when a regulator or board member asks about a specific AI system. Real-time visibility, a governed AI risk register and clear accountability are needed across every model, pipeline and agent.
AI governance, delivered as a service.
AI Governance as a Service (AIGRCaaS) is Cygeniq's service model for governance built around how AI actually behaves, not how software used to behave. Every AI asset is assessed against the frameworks that apply to it, re-evidenced as it changes, and turned into proof your regulators, auditors and board can review.
Every AI asset is assessed against the frameworks that actually apply to it, not a generic checklist. The assessment returns evidence, named gaps and concrete fixes, not just a score. Overlapping controls can be mapped once and evidenced across more than one framework.
When a model retrains, a data source changes or an agent gains a new permission, the asset is re-evidenced. Conformance becomes a standing state to maintain rather than a snapshot recreated before an audit. Real-time monitoring can also keep the risk and control picture current between assessment cycles.
Attestations, risk-register entries, control-level evidence and board-ready proof are produced from the assessment and maintained in a form that can support audit and regulatory review. Preparation time can then focus on reviewing evidence rather than rebuilding it from scratch.
Every AI asset, every framework in scope. LLM applications, RAG pipelines and agentic AI fail in different ways, so they need to be assessed differently. AIGRCaaS treats each as its own asset type with its own controls, rather than stretching one generic AI checklist across all three. GRCortex AI centralizes governance, risk and compliance coverage across the enterprise AI estate with lifecycle tracking and monitoring.
Safety and content testing, prompt hardening, output guardrails, and controls aimed at hallucination and toxicity. Generative AI applications are assessed for misuse, tool misuse, model security gaps and compliance risk specific to their deployment context, with controls tied to the model and application layers.
Data-source controls, retrieval integrity checks, PII handling, grounding checks and access-control mapping are assessed because a RAG system is only as trustworthy as what it retrieves and who can reach it. Third-party data integration and supply-chain risk in the AI data layer are mapped and evidenced.
Action sandboxing, human-in-the-loop gates, scoped permissions, ongoing monitoring and kill-switch evidence address the different risk created when an AI system can take action. Each agent is assessed against its operational context, permissions and accountability requirements across its lifecycle.
ISO/IEC 42001, NIST AI RMF and the EU AI Act form the baseline every asset is measured against. HIPAA, HITRUST, FDA AI/ML and regional AI guidance for the UAE, UK, RBI and Singapore are available as optional framework packs where they apply. Financial services and healthcare engagements can use framework packs aligned to sector requirements.
Two assessments on every AI asset: risk and compliance. Every AI asset follows the same disciplined sequence from classification to a confirmed fix, so governance is based on asset-specific evidence rather than guesswork.
Each asset is placed on the asset-type by complexity matrix to determine its AI risk profile, applicable compliance requirements and assessment scope, so the right depth of assessment is applied from the start.
A risk profile, harm model and control mapping are built for that specific asset. The assessment identifies threat exposure, liability and accountability gaps and aligns the findings to the existing security posture and compliance register.
The asset is checked for conformance against every framework pack in scope in one pass, with control-level evidence generated for each applicable standard and regulation.
Control gaps are prioritized by AI risk and obligation, providing visibility into where the AI governance and security posture is weakest and helping teams act first on the gaps that matter most.
Audit-ready evidence and risk-register entries are produced directly from the assessment in a format that can support certification, regulatory review, internal audit and board reporting. Where integrated, evidence can flow into existing governance and enterprise workflows.
One retest confirms that remediation actually holds, closing the loop between assessment and correction instead of leaving a fix unverified.
Govern once. Standardize. Assess every asset. Governance work gets duplicated when every new AI system starts its assessment from zero. AIGRCaaS separates what should be built once from what needs to happen for every individual asset.
The AI governance framework, risk taxonomy, policy hierarchy, control framework and central AI inventory are built once at the organizational level, so every later assessment draws from the same foundation instead of reinventing it.
LLM, RAG and agentic AI each get their own differentiated assessment template. A model that generates text, a pipeline that retrieves data and an agent that takes action have different failure modes, so one generic checklist is not enough.
Every deployment gets its own Risk and Compliance Assessment with evidence and remediation attached, delivered as an annual subscription so the work stays current as the asset itself changes. Real-time monitoring can maintain the evidence picture between assessment cycles.
Transparent pricing: a published rate card per assessment by AI asset type and complexity, with volume tiers, a Risk plus Compliance bundle and a 3-year rate lock. The model is designed to scale as the enterprise AI estate grows.
Controls mapped once, evidenced everywhere. A control assessed for ISO/IEC 42001 can overlap with requirements under NIST AI RMF or the EU AI Act. Reassessing the same evidence separately for every framework creates duplicate work. AIGRCaaS maps overlapping controls once and evidences them across the frameworks in scope, so one assessment can support several audit and regulatory needs.
Baseline frameworksGRCortex AI centralizes the AI compliance register, risk evidence and ongoing control picture.
Native integrations into existing cloud, security and enterprise workflows automate evidence collection and reduce audit-cycle friction.
Purpose-built, end-to-end AI governance and security, trusted by industry leaders, on one Runtime AI Trust Platform rather than a stack of point tools.
Security, runtime defense and governance run on one Runtime AI Trust Platform. GRCortex AI, Hexashield AI and CyberTix AI connect compliance evidence, AI security testing, real-time threat detection and AI runtime security instead of leaving teams to reconcile separate point tools.
Testing, runtime protection and control evidence feed into the same assurance picture so it stays current as AI changes instead of aging the moment an assessment is filed away. Real-time detection of threat, misuse and tool misuse across AI applications helps keep the security posture aligned with deployment.
The service is designed around OWASP, MITRE ATLAS, ISO/IEC 42001, NIST AI RMF and the EU AI Act, with additional framework packs for regulated sectors. The assessment is structured to speak the language auditors, regulators and risk teams already use.
Specialists across LLM, RAG and agentic AI deliver the work from the Cygeniq AI Delivery Center, applying differentiated methods to different AI asset types instead of treating every AI deployment the same way.
One classification model, one rate card and one refresh cycle apply whether the estate is a handful of pilots or hundreds of production systems. The approach is designed to grow with the AI estate without rebuilding the governance model at scale.
Before any assessment begins, Cygeniq classifies your AI estate, confirms which framework packs and jurisdictions apply, and configures the governance foundation everything else builds on. Most organizations leave the scoping workshop with real-time visibility into their AI security posture, a clear AI risk register and a compliance roadmap for the next 12 months. From pilot to production, the objective is governed, secure and compliant AI adoption.
Start with a Scoping Workshop
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