Enterprise AI Validation
Klarna replaced 700 customer service agents with AI. Costs dropped 40%. Then satisfaction collapsed, repeat contacts spiked, and Q1 2025 ended with a $99 million net loss. They rehired humans within months.
The problem was not the AI. It was what nobody validated: whether the AI could handle the 20% of interactions that actually drive brand reputation, regulatory compliance, and customer lifetime value. Most enterprise AI deployments share this blind spot.
70-85%
of enterprise AI projects fail to reach production
RAND, Gartner, BCG, McKinsey
EUR 35M
maximum EU AI Act penalty per violation
EU AI Act Article 99
95%
of AI pilots deliver no measurable P&L impact
MIT NANDA Study, 2025
The pattern repeats across industries. AI handles routine tasks well. It collapses on the edge cases that carry the most financial and regulatory weight.
2024: AI assistant handles 75% of chats across 35 languages. Cost per transaction drops from $0.32 to $0.19. Headlines celebrate the savings.
Early 2025: CSAT scores drop 22%. Customers hit what the press called a "Kafkaesque loop" on complex disputes, refunds, and financial advice. The AI handled password resets perfectly. It could not navigate a multi-currency refund involving a cancelled flight and a disputed merchant charge.
Mid-2025: Full reversal. Klarna reassigns software engineers and marketers to staff call centers. Q1 closes with a $99 million net loss despite 15% revenue growth. 55% of companies that replaced humans with AI now report regret (Orgvue/Forrester).
The lesson is not "AI doesn't work." Klarna's AI saved real money on routine transactions. The lesson is that nobody validated whether the AI could handle the interactions where failure costs more than the savings on everything else combined.
Generic guardrails catch toxicity and PII leakage. They do not catch an AI that miscalculates an insurance reserve, cites a repealed statute, or approves a loan that violates fair lending rules. On legal due diligence tasks, AI error rates run 69-88%. Toxicity filters would not flag a single one of those errors.
78% of employees use AI tools their employer did not provide. 77% share sensitive or proprietary data through those tools. Samsung and Amazon both discovered proprietary code in public AI services. The average shadow AI breach costs $4.63 million. Your governance platform cannot govern what it cannot see.
Gartner projects 40% of enterprise applications will embed autonomous AI agents by end of 2026. These agents modify databases, execute transactions, and send customer communications. Only one-third of organizations have governance maturity for agentic AI (McKinsey). The risk shifts from wrong answers to irreversible wrong actions.
The AI governance market is growing at 45.3% CAGR. There are real solutions available. Understanding what each does, and where each stops, is the first step toward closing the validation gap.
| Category | Examples | What It Does | Where It Stops |
|---|---|---|---|
| Policy & Governance Platforms | Credo AI, IBM watsonx.governance, ModelOp | Map AI initiatives to regulatory frameworks. Track compliance status. Generate audit reports. Credo AI ranked #6 in Applied AI by Fast Company 2026. | Policy compliance is not output correctness. A green dashboard does not mean the AI gives right answers for your specific domain. These platforms manage governance process, not technical validation. |
| Model Monitoring | Arthur AI, Galileo, Arize | Real-time drift detection, fairness metrics, latency tracking. Arthur AI added unified governance for agentic AI discovery in 2026. | Monitors model-level metrics (accuracy, token distribution, latency). Does not validate domain-level truth: whether that insurance calculation is correct given this policyholder's specific coverage terms. |
| AI Security | Cisco AI Defense (Robust Intelligence), Lakera, Promptfoo | Prompt injection detection, jailbreak prevention, data poisoning assessment. Cisco paid ~$400M for Robust Intelligence in Oct 2024. Mapped to OWASP and MITRE ATLAS standards. | Security validation is necessary but not sufficient. An AI that is secure against prompt injection can still hallucinate case law, miscalculate reserves, or violate fair lending rules. Safety is not correctness. |
| Guardrail Frameworks | NVIDIA NeMo Guardrails, Guardrails AI, LangKit | Programmable content moderation, PII detection, topic filtering. NeMo v0.20.0 added reasoning-capable safety and multilingual detection. | Self-check mechanisms depend on the same AI models they guard. No single framework handles all failure modes. Latency overhead per check affects real-time UX. Catches output format errors, not domain knowledge errors. |
| Big 4 / Large SIs | Deloitte, EY, Accenture, McKinsey | Enterprise-scale AI strategy, governance framework design, regulatory advisory. EY commercialized neuro-symbolic AI through its Growth Protocol partnership. | Strategy and framework design, not production validation engineering. Engagements run $500K-$5M+ and 6-18 months. Often recommend platforms rather than building custom validation. The deliverable is a PowerPoint and a vendor shortlist, not a running system. |
| DIY / Open Source | Garak, PyRIT, DeepTeam, custom test harnesses | Vulnerability scanning, automated red teaming, CI/CD integration. Free and transparent. | Requires ML infrastructure teams that 35% of enterprises have already built (Retool 2026). The remaining 65% need the testing capability without building the team from scratch. No regulatory documentation or compliance artifacts included. |
The gap in this table is vertical. Each row solves a piece. None solve the full stack: discovering all AI in the organization, validating domain-specific correctness, producing regulatory documentation, monitoring production behavior, and governing autonomous agent actions. That vertical integration, built for your specific industry and use cases, is what we do.
Every engagement is custom. These are the validation capabilities we build most often, shaped by the domain and regulatory environment each client operates in.
A middleware layer between your LLM and your business application. Pre-inference: intent classification, policy pre-check against your rule engine, prompt injection detection. Post-inference: output verification against domain-specific rules encoded in DSLs, JSON schema enforcement, citation verification against your knowledge base.
We reach for finite state machines for compliance workflows because they are provably correct. When your AI processes a mortgage application, the FSM guarantees that TRID disclosure timing, ECOA adverse action requirements, and flood insurance determinations happen in the right order. A probabilistic guardrail "usually" enforces this. An FSM always does.
Custom test suites built from your business rules, not generic benchmarks. If you are a bank using AI for credit decisioning, the test suite verifies adverse action notice accuracy, disparate impact ratios (the four-fifths rule requires your AI's approval rate for any protected group to be at least 80% of the highest group's rate), and HMDA data field correctness.
For insurance, we test ICD-10 code matching against policy exclusions, reserve calculations against actuarial tables, and subrogation determination logic. For legal, we verify every cited case exists, was not overturned, and actually supports the proposition it is cited for. These are the errors that generic monitoring misses and regulators find.
Systematic mapping of every AI touchpoint in the organization, including the tools your IT team does not know about. We analyze network traffic patterns, browser extension inventories, SSO/OAuth token grants, and API call signatures to produce a complete AI usage inventory.
Each discovered tool gets a risk classification: what data it accesses, whether it has acceptable use policies, and whether it should be blocked, brought under enterprise licensing with DLP controls, or left as-is. The harder deliverable is designing a sanctioned AI environment fast enough that employees stop routing around it. If the approved path requires three approval forms, people will keep using ChatGPT on their phones.
Technical infrastructure that produces the evidence regulators need. For banking: SR 11-7 model validation packages including conceptual soundness assessment, outcomes analysis against holdout datasets, ongoing monitoring specs with drift thresholds, and governance escalation procedures. For EU operations: Article 6 conformity assessment, risk management system documentation, and automatic logging architectures.
The documentation follows the format that OCC examiners and EU national authorities are trained to review. When a regulator asks how you validated your AI, you hand them the report. You do not scramble to reconstruct it after receiving the examination notice. The August 2, 2026 EU AI Act deadline for high-risk systems is four months away. If your AI touches credit, insurance, employment, or safety-critical functions, the clock is running.
For AI agents that take actions, not just generate text. We build accountability through four mechanisms: bounded autonomy (explicit tool allowlists with transaction limits), structured action audit trails (not application logs, but decision records a compliance officer can reconstruct weeks later), rollback procedures defined before deployment, and circuit breakers that suspend agents when behavior deviates from baseline.
A claims processing agent can look up policy details autonomously but cannot approve payments above $5,000 without human confirmation. That threshold is not arbitrary. It is calibrated to your specific error rate, regulatory exposure, and operational risk tolerance.
Red teaming goes beyond jailbreak detection. We run domain-specific adversarial campaigns that test decision correctness under edge cases. For lending: applicants with unusual income structures, conflicting credit signals, SCRA eligibility. For claims: multi-party disputes, subrogation scenarios, cross-jurisdictional coverage questions.
Each campaign produces a structured finding report with severity classification, reproduction steps, business impact, and remediation plan. We build continuous adversarial coverage into your CI/CD pipeline so tests run against every deployment candidate. LLM behavior changes with every model update, and yesterday's passing test may fail tomorrow.
Three phases. Not waterfall stages that happen once, but a continuous cycle. The validation architecture grows with your AI deployment.
We start by finding every AI system in the organization, including shadow deployments. Network traffic analysis, API call pattern detection, SSO token audits. The output is a risk-scored AI inventory with regulatory exposure mapped per system.
For each AI system that touches regulated decisions, we extract the business rules it should follow: lending policies, claims guidelines, compliance requirements, customer communication standards. These rules become the validation baseline. If they are not documented (common), we work with your subject matter experts to codify them.
Deliverable: AI inventory with risk classifications, regulatory gap analysis, and a prioritized validation roadmap. The roadmap puts the highest-exposure systems first.
We build domain-specific test suites for each priority system. The tests come from the business rules extracted in Phase 1, augmented by adversarial edge cases designed to expose failures that routine testing misses. Simultaneously, we build the deterministic validation layer: the middleware that enforces business rules at inference time.
Shadow mode deployment runs the validated system alongside existing operations for 4-8 weeks. We measure agreement rates, flag divergences, and build a statistical confidence profile. The system does not replace any human until the shadow data proves it handles the edge cases correctly.
Deliverable: Domain-specific test suites, deterministic validation middleware, shadow mode performance report, and SR 11-7 or EU AI Act compliance documentation for each validated system.
Production monitoring that tracks domain-level correctness, not just model-level metrics. When OpenAI updates GPT-4 without notice (behavior measurably changed between March and June 2023 on multiple benchmarks), your monitoring catches the drift before it affects decisions. When regulations change, the validation rules update.
Continuous adversarial testing runs in your CI/CD pipeline. Every prompt change, model update, or fine-tuning run triggers the full test suite. Red team campaigns run quarterly against the production system.
Deliverable: Production monitoring dashboard with domain-specific correctness metrics, automated regression testing pipeline, quarterly red team reports, and updated compliance documentation.
A note on timelines: Phase 1 is scoped tightly because it produces immediate value: you learn what AI is running in your organization and where the highest risks are. Many clients act on the Phase 1 deliverable before Phase 2 begins, shutting down high-risk shadow deployments or adding interim controls to exposed systems. Phase 2 timing depends on the number of systems and complexity of business rules. A single customer-facing chatbot validates faster than a multi-agent claims processing pipeline.
Answer seven questions about your AI deployment. The assessment produces a risk profile across four dimensions and specific next steps you can take immediately, with or without external help.
Question 1 of 7
Based on your responses. Use these findings to prioritize validation efforts.
Production validation requires three layers that most teams skip. First, domain-specific test suites: not generic toxicity or hallucination checks, but tests built from your actual business rules. If your AI processes insurance claims, the test suite verifies ICD-10 code accuracy, policy exclusion matching, and reserve calculation correctness against your underwriting guidelines.
Second, adversarial stress testing: we run your system against edge cases your training data never covered. What happens when a customer submits a claim in two currencies? When a contract references a statute that was amended last month? When an agent tries to process a transaction that requires two approvals but only one is present?
Third, shadow mode deployment: the AI runs alongside your human team for 4-8 weeks, processing the same inputs. We measure agreement rates, flag divergences, and build a statistical confidence profile before any human is removed from the loop. The validation report produced at each stage follows SR 11-7 documentation standards, so if your regulator asks how you validated the model, you hand them the report rather than scrambling to reconstruct it after the fact.
The August 2, 2026 deadline activates requirements for high-risk AI systems under Article 6 and transparency obligations under Article 50. If your AI system influences credit decisions, insurance underwriting, employment screening, or any safety-critical function listed in Annex III, it is high-risk.
High-risk systems must maintain a risk management system that runs throughout the AI lifecycle, not just at deployment. You need technical documentation covering training data provenance, model architecture decisions, and validation methodology. You need human oversight mechanisms that allow operators to override or shut down the system. You need automatic logging that captures every decision with enough detail for post-hoc audit.
Transparency obligations require that AI chatbots disclose their artificial nature, emotion recognition systems notify users, and deepfake content carries machine-readable watermarks. Penalties for non-compliance reach EUR 35 million or 7% of global annual turnover for prohibited practices, and EUR 15 million or 3% for high-risk system violations.
Finland became the first Member State with fully operational enforcement powers in January 2026, and other national authorities are standing up enforcement teams now. The practical gap most enterprises face is not understanding the rules but producing the technical evidence. Your risk management system needs to generate auditable artifacts, not just policy documents that sit in SharePoint.
Shadow AI is now the most common source of enterprise AI risk. Gartner found 69% of organizations suspect employees are using prohibited public GenAI tools, and 77% of employees admit to sharing sensitive or proprietary information with ChatGPT. Samsung and Amazon both discovered proprietary code uploaded to public AI services. The cost is not hypothetical: shadow AI breaches average $4.63 million, roughly $670,000 more than breaches at organizations with controlled AI usage.
Discovery is the first step. We map AI usage across the organization through network traffic analysis, browser extension audits, SSO/OAuth token analysis, and API call pattern detection. This produces a complete inventory of every AI touchpoint, including services accessed through personal devices and accounts that bypass corporate VPN.
The inventory feeds into a risk-scored classification: which tools handle sensitive data, which have acceptable use policies, which need to be blocked, and which should be brought under governance with enterprise licensing and data loss prevention controls.
The harder problem is creating a sanctioned alternative that employees actually prefer over shadow tools. If your approved AI solution requires three approval forms and a two-week wait, people will keep using ChatGPT on their phones. We help design governed AI access that is fast enough to compete with the shadow alternatives.
Most AI governance platforms (Credo AI, IBM watsonx.governance, ModelOp) focus on policy management: defining governance policies, mapping them to regulations, tracking compliance status across AI initiatives, and generating reports. This is necessary work, but it does not answer the question that matters most: does the AI actually give correct answers for your specific use case?
Governance tells you that you have a policy requiring 95% accuracy on claims processing. Validation tells you whether you actually hit 95%, and on which claim types you fall to 70%. The gap is analogous to the difference between having an ISO 27001 certification and actually being secure. The certification proves you have processes. Penetration testing proves the processes work.
In our experience building validation systems, the most dangerous state is what we call governance theater: a well-organized dashboard showing green checkmarks while the AI underneath is hallucinating policy numbers, miscalculating reserves, or citing statutes that were repealed two years ago.
Arthur AI and Galileo provide drift detection and monitoring, which is closer to validation, but they operate at the model metric level (accuracy, latency, token distribution) rather than the domain truth level (is this insurance reserve calculation correct given this specific policyholder's coverage terms).
SR 11-7 requires independent validation, comprehensive documentation, ongoing monitoring, and governance oversight for any model used in business decisioning. Applying this to LLMs introduces three complications that traditional model validation does not address.
First, vendor opacity: if you are using OpenAI or Anthropic APIs, the model provider will not share architecture details, training data composition, or weight updates. Your validation must be output-based, testing the model as a black box against your domain requirements. This means building challenger test suites that cover your specific use cases, not relying on the vendor's published benchmarks.
Second, non-stationarity: LLM providers update models without notice. GPT-4's behavior measurably changed between March and June 2023 on multiple benchmarks. Your validation documentation must include continuous monitoring that detects when model behavior shifts, and your governance framework must define what shift magnitude triggers revalidation.
Third, prompt sensitivity: small changes to prompts can produce dramatically different outputs. Your documentation must cover prompt versioning, A/B testing of prompt changes, and regression testing across your full test suite before any prompt modification reaches production.
We produce validation packages that include conceptual soundness assessment, outcomes analysis against holdout datasets, ongoing monitoring specifications with drift thresholds, and the governance escalation procedures that regulators expect to see. The documentation follows the format that OCC examiners are trained to review.
Agentic AI shifts the risk from wrong outputs to wrong actions. When an AI agent can modify a database, execute a financial transaction, send a customer communication, or approve a workflow, the failure mode is no longer a bad answer that a human can catch. It is an irreversible action that may violate policy, regulation, or common sense.
Only about one-third of organizations report maturity level 3 or above in agentic AI governance, according to McKinsey's 2026 assessment. The gap is structural: most governance frameworks were built for traditional models that score or classify, not for agents that plan and act.
We build agentic accountability through four mechanisms. Bounded autonomy: every agent has an explicit allowlist of tools it can invoke, with transaction limits and approval thresholds defined per action type. A claims processing agent can look up policy details autonomously but cannot approve payments above $5,000 without human confirmation. Action audit trails: every tool invocation is logged with the agent's reasoning chain, the input context, the action taken, and the outcome observed. This is not application logging. It is a structured decision record that a compliance officer can reconstruct weeks later.
Rollback capability: for any action the agent takes, we define the reversal procedure before deployment. If an agent sends an incorrect customer notification, the system must be able to issue a correction automatically. Circuit breakers: rate limits, anomaly detection on action patterns, and automatic suspension when the agent's behavior deviates from its baseline profile.
Most red teaming tools (Garak, PyRIT, Promptfoo) focus on security vulnerabilities: prompt injection, jailbreaking, data extraction, and content policy violations. This is important but insufficient for regulated enterprises. Security red teaming answers the question "can someone make the AI do something bad?" Business red teaming answers the question "does the AI do the right thing when the situation is complicated?"
We run domain-specific adversarial campaigns that test decision correctness under edge cases. For a lending AI, this means testing with applicants who have unusual income structures (seasonal workers, gig economy, trust fund distributions), conflicting credit signals (high income with recent bankruptcy), or regulatory edge cases (SCRA-eligible borrowers, community reinvestment obligations). For a claims processing AI, we test with multi-party claims, subrogation scenarios, policy exclusion ambiguities, and claims that span jurisdictional boundaries.
The test methodology follows a gray-box approach: we know the system's intended behavior and business rules, but we attack the implementation through the same interfaces a real user would encounter. Each test campaign produces a structured finding report with severity classification (critical, high, medium, low), reproduction steps, the business impact of the failure, and recommended remediation. We then retest after fixes to confirm the failure mode is resolved.
The cadence matters as much as the depth. LLM behavior changes with every model update, prompt modification, and fine-tuning run. We build continuous adversarial coverage into your CI/CD pipeline so that red team tests run automatically against every deployment candidate.
The research behind this solution page. For buyers who want to validate our depth.
Forensic analysis of the Klarna AI reversal, neuro-symbolic validation architectures, and the enterprise transition from probabilistic AI wrappers to deterministic validation layers.
Organizations lose $1M+ per hour during AI incidents (PagerDuty 2026). 729 documented AI hallucination incidents reached legal filings in 2025 alone.
Every week without domain-specific AI validation is a week where your highest-risk systems run on the assumption that generic guardrails are enough. The Klarna data says they are not.