Model risk, data risk, operational risk, and adversarial risk — quantified, registered, and continuously monitored under NIST AI RMF, integrated into your enterprise risk management program.
Enter your AI system endpoint and get an instant risk assessment across all 4 NIST AI RMF functions: GOVERN, MAP, MEASURE, MANAGE — with OWASP LLM Top 10 cross-mapping.
Every AI risk falls into one of four categories — each requires distinct controls and monitoring.
From risk register to board reporting, integrated with your existing enterprise risk program.
A living inventory of every AI risk across your model portfolio — likelihood, impact, owner, mitigation status, and review cadence — structured like a traditional ERM risk register.
Centralized Risk InventoryAssess foundation model providers, AI SaaS vendors, and embedded AI features in procured software for data handling, model provenance, and security posture before approval.
Vendor Due DiligenceValidation, challenger model testing, and ongoing performance monitoring for AI/ML models in regulated contexts — credit decisioning, underwriting, fraud detection.
MRM for BFSIProduction AI systems are monitored continuously for drift, anomalous output patterns, and emerging adversarial techniques rather than assessed only at deployment.
Real-Time TelemetryAI risk scores feed directly into your enterprise risk management dashboards alongside operational, financial, and cyber risk — one consolidated risk view for the board.
Unified Risk ReportingRed team assessment of deployed LLMs and ML models against prompt injection, jailbreaks, and extraction attacks mapped to the OWASP LLM Top 10.
OWASP LLM Top 10Traditional IT risk management was built for deterministic systems: a server either works or it doesn't, a firewall rule either blocks traffic or allows it. AI systems break this model. A large language model can perform correctly on 999 out of 1,000 inputs and fail unpredictably on the thousandth in ways that are difficult to anticipate, reproduce, or fully eliminate. This probabilistic, non-deterministic nature means AI risk requires a distinct taxonomy, distinct measurement approaches, and continuous — not point-in-time — assessment.
Model risk concerns the AI system's own behavior: does it perform as intended, does its accuracy degrade over time as the world changes around static training data (concept drift), and does it hallucinate plausible-sounding but false outputs. Data risk covers everything upstream and downstream of the model — was training data poisoned by an adversary, does the model leak memorized personally identifiable information when prompted cleverly, and can data provenance be traced and audited. Operational risk addresses the AI system as a piece of production infrastructure — third-party API dependency failures, uncontrolled model version upgrades from a vendor that silently change behavior, and latency or cost overruns that disrupt business processes. Adversarial risk is the attack surface unique to AI: prompt injection that hijacks a model's instructions, jailbreaks that bypass safety training, model extraction attacks that steal proprietary model weights through repeated querying, and membership inference attacks that determine whether specific data was used in training.
The NIST AI Risk Management Framework's four functions provide the operational backbone for AI risk programs. GOVERN establishes organizational accountability — who owns AI risk decisions, what risk appetite has been set, and how AI governance integrates with existing risk committees. MAP identifies risk context for each AI system — its intended use, deployment environment, and potential impact if it fails. MEASURE applies quantitative and qualitative methods to assess identified risks — bias testing, robustness evaluation, red team exercises. MANAGE closes the loop with risk treatment — accepting, mitigating, transferring, or avoiding each identified risk, with documented decisions and ongoing monitoring commitments.
Financial institutions have practiced model risk management for over a decade under guidance like the US Federal Reserve's SR 11-7, which mandates independent model validation, ongoing performance monitoring, and clear model inventories for any model used in business decisions. As AI and machine learning models increasingly drive credit decisioning, fraud detection, and underwriting, this same MRM discipline must extend to them — including challenger model testing (running a shadow model alongside production to detect divergence), explainability requirements for adverse credit decisions, and documented model risk tiering based on business impact. Insurance, healthcare, and other regulated sectors face analogous requirements, even where formal MRM guidance is less codified than in banking.
Most organizations do not train their own foundation models — they consume AI capability through APIs, embedded SaaS features, and pre-trained model weights from external providers. This creates a vendor risk surface that traditional third-party risk management programs are often unequipped to assess: does the vendor retain your data for training their next model version, what happens when the underlying foundation model is silently upgraded and changes behavior, and does the vendor's model carry inherited biases or vulnerabilities from its own training data. A rigorous AI vendor risk assessment evaluates data handling commitments, model change-management practices, incident history, and security posture before an AI vendor is approved — and reassesses periodically as vendor terms and models evolve.
An AI risk register operationalizes the taxonomy above into a living inventory: every AI system in use, its risk classification, identified risks across all four categories, assigned risk owner, current mitigation status, and next review date. This mirrors the structure of a traditional enterprise risk register but with AI-specific fields — model version, training data cutoff, last red team assessment date, and drift monitoring status. Critically, the register must be a living document tied to continuous monitoring telemetry, not a static spreadsheet updated once a year, because AI risk profiles change as models are retrained, fine-tuned, or as new adversarial techniques are published.
AI risk cannot live in a silo separate from the rest of enterprise risk management. A model failure in a customer-facing chatbot is ultimately an operational and reputational risk that belongs on the same board risk dashboard as a data center outage or a supply chain disruption. Mature organizations integrate AI risk scores directly into their ERM platform, using common risk scoring methodologies (likelihood × impact, or more sophisticated quantitative models) so that AI risk can be compared, prioritized, and reported alongside every other category of enterprise risk — rather than existing as a disconnected technical concern that never reaches the board.
A risk assessment performed before deployment captures the model's risk profile at a single moment. Production AI systems drift: input distributions shift, adversaries develop new jailbreak techniques, and fine-tuning updates change behavior in subtle ways. Continuous AI risk monitoring tracks model output quality, anomalous request patterns indicative of adversarial probing, and emerging attack techniques relevant to the deployed model architecture — feeding directly into the same threat intelligence engine, including CISA KEV and EPSS-informed CVE tracking, used elsewhere on this platform.
Technical AI risk findings — a jailbreak success rate, a hallucination frequency, a drift metric — are meaningless to a budget-holding executive unless translated into business terms: likelihood of a costly incident, potential regulatory exposure, reputational impact, and the cost of mitigation versus the cost of inaction. Quantifying AI risk requires the same discipline applied to traditional enterprise risk: assigning likelihood and impact scores, expressing exposure in terms business leaders already use for other risk categories, and tracking how that exposure changes as mitigations are implemented or as the AI system's usage scales. Organizations that skip this translation step often find AI risk initiatives stall at the technical team level, never securing the budget or organizational priority needed to actually close identified gaps.
Effective AI risk management requires clear ownership that spans technical and business functions — a model owner accountable for performance and drift, a data owner accountable for training and inference data quality, a security owner accountable for adversarial robustness, and a business owner accountable for the use case's overall risk acceptance. Many organizations create a cross-functional AI governance committee or AI risk council that meets on a defined cadence to review the risk register, approve new high-risk AI use cases before deployment, and make risk acceptance decisions that individual technical teams shouldn't make unilaterally. This governance structure mirrors how mature organizations already handle other enterprise risk categories — financial risk, operational risk, cyber risk — and extending the same discipline to AI risk avoids treating it as a uniquely ungovernable technology category.
When an AI system fails in production — generates harmful content, leaks sensitive data through a prompt injection attack, or makes a consequential decision based on a hallucinated fact — the organization needs a response plan as well-rehearsed as its traditional security incident response plan. This includes predefined criteria for what constitutes an AI incident severe enough to trigger response, a clear escalation path to technical and legal stakeholders, a process for quickly disabling or rolling back a misbehaving model, and post-incident analysis that feeds back into the risk register and model validation process.
It's tempting to treat AI risk as simply another flavor of IT risk and apply existing frameworks unchanged, but several characteristics genuinely distinguish it. AI systems can fail silently and gracefully — producing a confidently wrong answer rather than an obvious error message — making failure detection fundamentally harder than traditional software where a crash or exception is unambiguous. AI systems also exhibit emergent behavior that wasn't explicitly programmed, meaning risk assessment can't rely solely on code review the way it might for deterministic software; behavioral testing against representative and adversarial inputs becomes essential. Finally, AI risk often compounds with scale in non-linear ways — a model with a 1% hallucination rate is a minor concern at low query volume but becomes a significant operational risk once that model handles millions of customer interactions, since the absolute number of bad outcomes scales with usage even as the rate stays constant.
Static documentation review and architecture analysis can identify some AI risks, but the most reliable way to measure actual adversarial robustness is to attempt to break the system the way a real attacker would. Red teaming a deployed LLM or ML model — attempting prompt injection, jailbreak techniques, data extraction, and other adversarial inputs mapped to the OWASP LLM Top 10 — produces empirical risk data rather than theoretical risk estimates. This measured approach feeds directly into the MEASURE function of NIST AI RMF and gives risk owners concrete evidence to support risk acceptance, mitigation investment, or deployment delay decisions, rather than relying on vendor assurances or generic industry benchmarks that may not reflect how the specific model behaves in your specific deployment context.
AI risk management is itself a discipline that should mature over time. Early-stage programs often start with a basic inventory and ad hoc risk assessments; mature programs evolve toward standardized risk scoring methodologies applied consistently across every AI system, automated drift and anomaly detection integrated into production monitoring, and a feedback loop where lessons from AI incidents and red team findings systematically improve the risk assessment process for the next AI system being evaluated. Treating the risk program itself as something to be measured and improved — not just the AI systems it governs — is what prevents AI risk management from calcifying into a one-time compliance checkbox exercise that fails to keep pace with how quickly AI capability and associated risk continue to evolve.
Much of the current attention on AI risk centers on large language models, but predictive ML models — credit scoring, fraud detection, demand forecasting, churn prediction — carry their own well-established risk profile that predates the generative AI wave by years. These systems face data drift as customer behavior changes, fairness and bias risk when training data reflects historical discrimination, and explainability requirements for any model influencing decisions that affect individuals' access to credit, employment, or services. A comprehensive AI risk program must cover this entire model portfolio, not just newly deployed generative AI applications — organizations that focus AI risk attention exclusively on chatbots and copilots while leaving years-old predictive models unmonitored are missing a substantial portion of their actual AI risk exposure.
See Threat Intelligence for the underlying intelligence engine and OWASP LLM Security for adversarial testing methodology specific to large language models.
Run a free security assessment and get a baseline AI risk profile across model, data, operational, and adversarial categories.