01
Incomplete AI inventory
Vendor-embedded AI, decisioning tools, and operational models remain outside the governed inventory.
AI governance, made defensible
Turn complexity into clarity.
AuditAlign maps internal controls to regulatory frameworks, finds the gaps, and builds the evidence trail your examiners expect.
3 regulatory frameworks mapped automatically
Deterministic engine — no LLM makes compliance calls
Built by a former federal bank examiner
93% accuracy — blind-tested on 115 labeled controls
Mapping controls and requirements
$270B
spent annually by financial institutions on compliance — and costs have risen 60%+ over the past decade
LexisNexis Risk Solutions · True Cost of Financial Crime, 2023
257
regulatory updates tracked per day on average — up from just 10 per day in 2004
Thomson Reuters Regulatory Intelligence, 2022
2.71×
the cost of non-compliance vs. running the compliance program — making a single finding more expensive than prevention
Ponemon Institute · Cost of Non-Compliance Study
Where reviews break down
Institutions often have controls. What they lack is demonstrable coverage.
02
No requirement-level mapping
Policies name a framework but do not show which specific controls satisfy each obligation.
03
Validation does not fit AI
Traditional model procedures are applied without adapting testing for GenAI, ML, and non-deterministic systems.
04
Board reporting lacks evidence
Senior management sees activity and inventory counts rather than coverage, open gaps, and examination exposure.
The enforcement reality
The numbers make the case better than we can.
$19.3B
in global bank fines in 2024 — a record high, driven by governance failures examiners could document
$3.09B
TD Bank penalty — the largest AML fine in U.S. banking history. Root cause: governance frameworks that could not withstand examiner scrutiny
2.71×
the cost of non-compliance versus running a compliance program — making a single enforcement action far more expensive than the tool that prevents it
417%
surge in regulatory penalties in H1 2025 vs. the same period in 2024. The enforcement environment is accelerating.
12%
of financial services firms using AI have adopted a formal AI risk management framework — leaving 88% exposed going into the examination cycle
18%
have a formal AI model testing program in place — leaving model outputs unvalidated at the vast majority of institutions
The institutions that receive findings are not those without governance — they are the ones that cannot demonstrate it in the format an examiner expects. Policies that reference NIST by name but contain no requirement-level mapping are not defensible. AuditAlign closes that gap.
Confidence through alignment
Governance evidence should be as rigorous as the decisions it supports.
AuditAlign replaces weeks of manual comparison with a clear, repeatable view of how your controls meet NIST AI RMF, CRI FS AI RMF, and emerging supervisory expectations.
3regulatory frameworks mapped automatically
10workbook views built for risk, audit, and executive teams
1shared source of truth from requirement to remediation
What AuditAlign does
One platform. A clearer path from policy to proof.
Connect controls to requirements
Compare every internal control against each framework requirement with transparent, reciprocal semantic matching.
See the workflowUnderstand every finding
Move beyond a score. Review source text, matching rationale, confidence, and the precise reason coverage is partial or absent.
Explore evidenceClose gaps with confidence
Assign ownership, track remediation, attach evidence, and preserve a clean history for management and examiner review.
Start a pilotThe alignment engine
Six steps from document upload to exam-ready, defensible compliance.
01 · Ingest
Upload your controls and policies
Provide your AI governance policies, model risk procedures, and internal control documentation. No reformatting required — any format is accepted.
02 · Compare
Deterministic matching engine
Sentence-level embeddings and reciprocal matching score every control against every framework requirement. No LLM makes any compliance determination — the algorithm does. Results are identical every run.
03 · Classify
Every requirement classified
Each requirement receives a numeric similarity score classified against fixed thresholds — a finding your team can trace, reproduce, and defend in front of an examiner.
Control maps clearly✓ Confirmed
Coverage incomplete~ Partial
No control found✗ Gap
04 · Analyze
Gap scoring and coverage view
A 10-tab workbook surfaces coverage by framework, domain, maturity level, and examiner category. Gaps are ranked by risk and examination exposure so your team knows where to act first.
05 · Act
Assign ownership and remediate
Attach corrective action plans, assign owners, set target dates, and track closure. Every change preserves a clean history your management and examiners can review.
Third-party AI monitoring
45%
AI incident escalation
20%
Human override testing
68%
06 · Export
Examiner-ready workbook output
Export structured findings in MRA/MRIA format with source rationale, coverage data, and the full audit trail. Three report types built for board, analyst, and examiner audiences.
Supported frameworks
Built around the standards financial institutions are being asked to demonstrate.
NIST AI Risk Management Framework
The cross-industry reference model organized around Govern, Map, Measure, and Manage, with 72 requirements mapped automatically.
72 requirementsFinancial Services AI RMF
Purpose-built for financial services and co-developed with 108 institutions, with maturity-aware scoping from Initial through Embedded.
21 to 230 objectives by maturity stageSR 26-2 Crosswalk
Findings and rationale are framed around effective challenge, validation sufficiency, inventory, and governance documentation.
Exam-oriented language and evidenceDecision-ready outputs
The same analysis, written for every level of review.
Board and senior management
Executive Report
A concise view of coverage, highest-risk gaps, examination exposure, remediation priorities, and decisions requiring executive sponsorship.
Built for board packages, risk committees, executive readouts, and examination preparation.
Second line and internal audit
Analyst Report
Requirement-level mappings, source controls, similarity scores, classification rationale, framework coverage, and the evidence behind every conclusion.
Built for MRM analysts, compliance teams, internal audit, and effective challenge.
Examiner-style findings
Findings Report
Gaps written in an MRA/MRIA-style structure: supervisory concern, supporting facts, risk and impact, required corrective action, responsible owner, and target remediation date.
Structured in the way an examiner could cite, communicate, and track a formal finding.
One evidence base
Each report traces back to the same scored analysis and source documentation. Executives see the decision, analysts see the methodology, and reviewers see the facts and corrective action without conflicting versions of the story. Reports are accompanied by the structured workbook, gap register, visual summaries, and full run metadata.
AI Governance CoverageAuditAlign / 2026
AI risk taxonomy approvedNIST GOVERN 1.1Confirmed
Model inventory ownershipCRI GV-2.3Confirmed
Third-party AI monitoringNIST MAP 3.5Partial
AI incident escalationCRI MG-4.1Gap
Human override testingNIST MEASURE 2.7Partial
Every conclusion linked back to the evidence.
Built for scrutiny
Show the work behind every answer.
AuditAlign is designed for the moment a reviewer asks, "How did you reach that conclusion?" Each result preserves the source language, match logic, status, and supporting rationale.
Deterministic compliance classification
Source-level traceability and rationale
Review, override, and audit history
Excel output shaped for examiner workflows
Blind-tested accuracy
Measured against ground truth, not marketing claims.
93%
Overall classification accuracy
107 of 115 labeled bank controls were placed in the correct Confirmed, Partial, or Gap category in a blind benchmark.
100%
Confirmed-match precision
Testing produced zero false confirmations. When the engine called a control confirmed, every result was supported by the labeled answer.
20/20
Routine controls screened out
Non-AI banking controls such as wire callbacks, HMDA filings, cash handling, and BSA reporting were correctly excluded.
Benchmark: 115 labeled bank controls spanning direct matches, partial coverage, AI-adjacent decoys, and routine banking operations. Current engine version.
Security built for bank scrutiny
Protected by design. Accountable by default.
AuditAlign treats your controls, evidence, and compliance findings as confidential institutional information at every stage.
✓No customer or consumer data required.
AuditAlign does not collect PII, NPI, account, card, or transaction data.
AuditAlign does not collect PII, NPI, account, card, or transaction data.
Layered encryption
Per-tenant envelope encryption protects sensitive control text on top of AES-256 database encryption. Data in transit uses TLS 1.2 or higher.
Tenant-specific keysInstitution-level isolation
Every request is scoped to the authenticated institution. Private evidence files use tenant-isolated storage and time-limited download links.
No cross-tenant accessTraceable access
An append-only audit trail records sensitive reads, exports, changes, evidence submissions, and rationale overrides with user and UTC timestamp.
Read and write historyNever used for AI training
Your data is used only to provide the requested service. It is never used to train, fine-tune, or benchmark AuditAlign or third-party AI models.
No model trainingU.S.-hosted infrastructure. Institution data is stored in AWS us-east-1 through a SOC 2 Type II and ISO 27001 certified infrastructure provider. Mutual NDAs and vendor security reviews are supported.
Request security materialsBuilt from both sides of the examination table
"Most institutions do not lack commitment to AI governance. They lack the infrastructure to demonstrate it."
AuditAlign was built after years spent first as a federal bank examiner and then in second-line model risk roles. The recurring problem was not an absence of governance. It was the weeks of manual work required to connect policies and controls to what a reviewer needed to see.
The algorithm makes the compliance determination; the LLM only makes the explanation readable. Fixed thresholds, reciprocal matching, model version, and run metadata preserve a result your team can reproduce and defend.
Ways to start
Choose the evaluation path that fits your institution.
Co-creation / Limited availability
Design Partner
No-cost initial engagement
For institutions willing to help shape the product in exchange for a complete first analysis and direct access to the founder.
One NIST or CRI gap-analysis run
Full 10-tab workbook and rationale
60-minute findings debrief
Apply as a design partnerStructured evaluation / 8 weeks
Paid Pilot
$35,000 flat engagement
For teams that need live platform access, repeat analyses, remediation workflow, and a clear executive decision point.
Up to five named users
Up to three runs across both frameworks
Kickoff, office hours, and executive readout
25% credited toward year one on conversion
Start a pilotCommon questions
What institutions ask before they start.
A manual gap analysis requires a subject-matter expert to read every internal control against every framework requirement — a process that typically takes weeks and produces results that are hard to reproduce or defend. AuditAlign automates the comparison using a deterministic matching engine: every control is scored against every requirement in both directions, using fixed thresholds your team can audit. The output is a structured, version-stamped workbook with source rationale attached to every finding — not a consultant's judgment call.
No. Compliance determinations are made by the algorithm, not a language model. The engine uses sentence-level embeddings and reciprocal matching logic to produce a numeric similarity score for every control-requirement pair. Fixed thresholds convert those scores into Confirmed, Partial, or Gap classifications. An LLM is used only to make the written rationale readable — it never determines the classification. Every finding is reproducible from the same inputs.
Your control data is used solely for the analysis you request. It is never used to train, fine-tune, or benchmark AI models. Design Partner and Paid Pilot engagements can operate under mutual NDA with written confidentiality terms. AuditAlign also supports SIG Lite, CAIQ, and institution-specific security questionnaires for formal vendor evaluation processes.
Yes. Most institutions that come to AuditAlign have AI-adjacent governance language scattered across model risk policies, vendor management procedures, and board-level frameworks — without a structured inventory. The Design Partner engagement includes a scoping conversation to help you identify and organize what you already have. The resulting gap analysis often becomes the foundation for building a formal AI control inventory.
The analysis itself runs in minutes once your controls are ingested. The Design Partner engagement is structured around a single analysis run with a 60-minute findings debrief. The Paid Pilot is an 8-week structured evaluation that includes up to three analysis runs, remediation workflow, and an executive readout — giving your team time to validate findings, test remediation tracking, and build the internal business case.
Know your gaps before an examiner names them.
The Design Partner engagement is no-cost. The Paid Pilot is $35,000. A single enforcement finding costs multiples of both.