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An end-to-end AI model validation solution for regulated enterprises. DVS Validator combines the automated evaluations, real-time guardrails and agent security of the Dynamo AI platform with a bank-grade model risk governance framework — so you can rate, validate, approve and monitor traditional AI, generative AI and agentic AI models with audit-ready evidence.
Why automated tooling alone is not model validation — and why governance alone is too slow.
Regulated enterprises must demonstrate that every AI model — from credit scorecards to GenAI copilots and autonomous agents — is fit for purpose, tested, monitored and approved. Modern AI platforms automate testing but don't tell you how much validation a model needs, who must approve it, or what evidence a regulator expects. Governance playbooks answer those questions but execute slowly by hand.
DVS Model Risk Validator layers a proven, second-line validation operating model on top of Dynamo AI's automated evaluation and guardrail engines. Risk-rate each model, scale the depth of testing to its rating, execute the tests automatically, and produce a standardised validation report with findings, remediation actions and ongoing monitoring — end to end.
DVS Validator uses the Dynamo AI product suite as its execution engine. Three products cover the pre-production, post-production and agentic phases of every AI system.
Pre-production test & evaluation suite: hallucination detection with root-cause analysis, PII & data-leakage testing, adversarial security red-teaming, and custom policy-adherence evaluations. Every test auto-generates a detailed PDF report for audit — run from terminal, notebook or web app.
Post-production, low-latency guardrails authored in natural language: real-time hallucination checks, jailbreak and prompt-injection blocking, PII detection, toxicity filtering across 15+ content categories, and keyword monitoring — deployable in your VPC, on-prem or at the edge, with single-pane observability.
Evaluations and runtime security for AI agents and MCP tooling: automated risk discovery across tool combinations, "Lethal Trifecta" attack-path analysis, scope reduction, per-tool-call policy enforcement (allow / deny / human approval) and continuous governance with version-controlled policies.
Dynamo AI, DynamoEval, DynamoGuard and AgentWarden are products and trademarks of Dynamo AI. DVS Model Risk Validator is an independent solution and methodology layer delivered by Digital View Solutions Limited on top of the Dynamo AI platform; Digital View Solutions Limited is not affiliated with Dynamo AI.
Every engagement starts by answering one question: how much validation does this model deserve? DVS assigns each model a Model Risk Rating (MRR) from two dimensions.
The potential impact if the model fails or performs poorly. Assessed quantitatively where the model has measurable financial impact (revenue, savings, exposure against calibrated thresholds) and qualitatively where it does not — considering the proportion of clients impacted, the impact on each client, and business criticality. Models used only for supplementary decision-making can receive an optional notch-down.
Ratings: Insignificant · Low · Medium · High
The likelihood of an adverse event, driven by inherent risk factors: novelty and complexity of the modelling approach, reliability of inputs and training data, limitations of use, robustness (sensitivity of outputs to small input changes), and outstanding findings from previous reviews. Assessed as a structured checklist so conclusions are consistent and defensible.
Ratings: Low · Medium · High
Materiality and uncertainty combine into the final Model Risk Rating, which determines how much scrutiny, how frequent revalidation, and which approvals a model requires. High-risk models get more scrutiny and more frequent revalidation; low-risk models get lighter oversight.
| Uncertainty ↓ / Materiality → | Insignificant | Low | Medium | High |
|---|---|---|---|---|
| Low | Insignificant | Low | Low | Medium |
| Medium | Low | Low | Medium | High |
| High | Low | Medium | High | High |
Illustrative matrix. For multi-entity groups, materiality is rated in each regulated local context as well as at enterprise level; the overall MRR is the higher of the two. Guideline deviations are permitted with documented rationale from the lead validator.
Model owners test and document: business alignment, reproducibility, feature engineering, error analysis, explainability, robustness and monitoring capability.
Independent validation reviews every model before production, raises findings where gaps exist, and confirms compliance with your model risk policy.
Internal audit verifies that both the first and second lines are complying with policy — supported by DVS's complete evidence trail.
Validation is not a one-time gate. DVS manages seven review types across the life of every model, each with defined deadlines and deliverables.
Independent, end-to-end assessment before production deployment — conceptual soundness, data, testing evidence and monitoring plan.
Yearly confirmation that the model still works as expected and remains policy-compliant.
Any change to an approved model triggers an update review before the new version goes live.
Lighter-touch conceptual reviews for in-scope changes that don't require independent re-testing.
Periodic full re-validation on a cycle scaled to the model's risk rating.
Findings are improvement requests with owners and deadlines — remediation evidence is reviewed and closed formally.
Ongoing metric reporting: breaches, root causes and remediation actions submitted on schedule.
Standardised packages: materiality assessment, purpose & scope, model specification, versioned code, data access, quantitative testing evidence and a monitoring plan with thresholds.
Every DVS engagement produces a consistent validation report: executive summary, model purpose, validation conclusions, a finding table with severity classification, precise finding descriptions and non-prescriptive remediation actions — written to a professional style guide, calibrated across reviews for consistency, and finalised through a governed approval workflow (business owner, development owner and approval authority).
The independent testing core — powered by DynamoEval, scoped by the model's risk rating.
Detect and categorise hallucination types, trace underlying causes, and remediate with explainable, actionable insights — using hallucination definitions customised to your risk profile.
Find PII exposure and data-extraction vulnerabilities before attackers do; verify de-identification and test resistance to training-data extraction.
Adversarial testing against 20+ continuously updated jailbreak and prompt-injection vulnerability classes, for both generative and agentic systems.
Evaluate model behaviour with and without enforced policies, quantify policy impact, and check adherence to your specific compliance requirements — mapped to emerging regulation such as the EU AI Act.
Statistical performance evidence, benchmark and challenger comparisons, and commentary on acceptability against business expectations — the quantitative backbone of the validation report.
Every test auto-generates a detailed PDF report; human-in-the-loop review lets validators adjust and approve results so the final evidence pack is regulator-ready with zero manual formatting.
Validation conclusions become runtime controls — powered by DynamoGuard, reported through DVS quarterly monitoring.
Legal, risk and compliance teams author guardrails in natural language; AI-assisted policy writing ensures comprehensive coverage without engineering bottlenecks.
Low-latency checks catch hallucinations, compliance violations, PII leakage, toxicity and security attacks in production — before they reach users.
Log, audit and monitor every interaction across home-grown and third-party AI use cases in one unified platform, with actionable compliance insights.
VPC, on-prem or edge deployment; CPU-friendly inference; human-in-the-loop review of guardrail decisions for accuracy and trust.
DVS defines the monitoring plan at validation time — metrics, thresholds, sample methodology, frequency and responsible owners. Production metrics from the guardrail layer feed quarterly monitoring reports and the annual assessment, so drift, breaches and emerging risks surface early and are remediated on schedule.
Agents connected to enterprise tools create attack surfaces traditional security cannot see. DVS validates agents as rigorously as models — powered by AgentWarden.
Data exfiltration needs three capabilities in combination: untrusted input (an injection vector), private data access (an exfiltration target) and external communication (an exfiltration channel). Individual tools can look safe while their combination creates a complete attack path. DVS evaluates tool combinations, not just tools.
Automated risk discovery tags every tool an agent can reach and produces a prioritised risk report in minutes. Scope reduction removes unused high-risk tools — typically eliminating the vast majority of attack paths before any policy is needed. Runtime enforcement then applies allow / deny / human-approval decisions per tool call, with continuous re-evaluation as tools and permissions change.
Agents enter the same DVS lifecycle: risk-rated by materiality and uncertainty, fully validated before deployment, monitored via policy-violation alerts and agent-trajectory analysis, and re-validated when their toolsets change. Integrates across MCP clients and servers (development environments, ticketing, code hosting, CRM, chat and more) and with enterprise network security providers.
DVS Model Risk Validator is delivered as a consulting-led engagement on your Dynamo AI environment, or as a managed validation service.
Model inventory, risk-rating of your AI estate, gap analysis against regulatory expectations, and a prioritised validation roadmap.
Independent validation of your priority models — automated evaluations, documented findings, remediation plans and audit-ready reports.
Ongoing operation: guardrail management, quarterly monitoring, annual assessments, agent re-evaluations and regulator-facing evidence.
Every engagement is scoped to your estate and regulatory context.