Accountable AI & Governance
Transparent reporting and governance.
Coa builds governance, accountability, and traceability into every AI product we ship, so executives and program leaders can measure efficacy, drift, and performance. We can help you do the same. The approach is laid out below.
Operating principles
The five rules the AI works within.
Each rule is enforced in code and configuration — not prompt text — and holds across every product and every model.
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AI proposes. People decide.
Every output is a proposal a person accepts, edits, or rejects. The model never acts on its own.
Application: Every proposal arrives with its basis; a rejection is recorded with its reasoning, not discarded.
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Layered guardrails on every model.
Every prompt and every response is screened — grounding, prompt attacks, harmful content, sensitive data — before anyone acts on it.
Application: The same checks run whichever model does the work (e.g. Amazon Bedrock Guardrails).
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Secure by design, isolated by default.
Encrypted in transit and at rest, least-privilege, scoped per case. Your data stays inside your boundary.
Application: Per-tenant and per-case isolation, AWS KMS encryption, private connectivity — and customer data never trains a shared model.
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Everything on the record.
Every action is logged — the model, the inputs, the confidence, and what the person did with it. Reversible, reconstructable.
Application: An append-only history replays any action exactly as it happened.
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Configurable and self-hostable.
Which model does the work is a setting. Managed, self-hosted, on-prem, or GovCloud — sensitive data never leaves to be read.
Application: Your model or ours, chosen per deployment and per task.
Forward-based engineering
How we design, build, and run the system.
AI does the slow parts of building software; engineers own every line. Automated gates catch regressions, bugs are fixed continuously, the running system is monitored, and what we learn in production feeds the next feature.
Catch ambiguity before code
Doc-driven design
We write the feature as a specification and a real, interactive mockup before any code is committed. Design tokens and a binding reference spec keep every surface consistent and accessible by construction.
- One shared design system across product, docs, and site
- Mockups are the UI source of truth; the app is coded to match them
- Accessibility considered at design time, not bolted on
Speed without ceding judgment
AI-accelerated build
AI handles the work that usually makes software slow (scaffolding, encoding rules and forms, building connectors) while our engineers review and own every line. The build moves faster, and the judgment stays with the people who write the code.
- Section 508 / WCAG conformance generated and checked as we build
- Regulations and forms encoded as configurable rules
- Connectors scaffolded and tested quickly
Every change proven before it lands
Automated quality gates
Every change clears the same automated gate before it merges, running static type-checking, unit and integration tests, a real-browser render gate that loads the actual UI, accessibility checks, and a security review.
- Typecheck + unit/integration tests
- Real-browser render gate (loads the actual app, not a mock)
- Section 508 / WCAG checks
- Automated security review on every change
Defects caught before users feel them
Continuous & automated bug-fixing
Automated review agents and regression tests run against every branch, and every confirmed fix becomes a permanent test so the same defect cannot return.
- Automated multi-agent code review on changes
- Regression added for every confirmed fix
- Continuous integration on every branch
Know before they tell you
Proactive monitoring
Live systems are watched continuously through application and model telemetry, drift and quality signals, and alarms that fire on anomalies, so problems surface from the data rather than a support ticket.
- Model + application observability
- Drift and quality-degradation signals
- Alarms on anomalies
The product improves from use
Feature development loop
What we learn in production (acceptance rates, where reviewers edit, what they reject) feeds the backlog. Changes are validated against the evaluation harness and rolled out behind scaled dry-runs before they reach everyone.
- Acceptance / edit / reject telemetry drives the backlog
- Changes validated against the eval harness
- Scaled dry-run before broad rollout
AI weaknesses & guardrails
Failure modes and the controls that contain them.
For each known LLM failure mode, we show the standard that names it, the control that addresses it, the enterprise service that implements the control, and what Coa adds on top.
Hallucination / fabricated facts OWASP LLM09 · NIST MEASURE
A contextual grounding check scores grounding and relevance against the source on a configurable 0–0.99 threshold and blocks answers the source does not support; every answer must carry citations. Grounding proves an answer is supported by the record, not that the conclusion is correct.
Amazon Bedrock Guardrails (contextual grounding blocks; automated reasoning checks are detect-only); Bedrock Knowledge Bases
Every output is grounded to page-level source chips, and nothing becomes final until a reviewer accepts it. Grounding catches unsupported claims; the legal conclusion rests with the adjudicator and is measured against known-correct outcomes in evaluation, never assumed from a fluent answer.
Prompt injection / jailbreak OWASP LLM01
Prompt-attack detection runs on both input and output, the guardrails apply independently of which model is used, and tools are granted only least-privilege scopes.
Amazon Bedrock Guardrails (prompt-attack filter); ApplyGuardrail API
The AI has no autonomous authority, so an injected instruction can only propose something for a person to review rather than carry it out. Isolating each case keeps any damage contained to that one case.
Sensitive-data / PII disclosure OWASP LLM02
PII is detected and masked in the model’s input and output, with data encrypted in transit and at rest and the case kept inside the deployment’s account and region. Masking only covers what the model sees, so logs and traces, which can retain raw text, are protected separately by data-protection policies and least-privilege access.
Bedrock Guardrails (sensitive-information filter); Amazon Comprehend; Amazon Macie; CloudWatch Logs data protection; AWS KMS
Retrieval is scoped to the case in front of the reviewer and customer data is never used to train or fine-tune a shared model; the case never leaves the deployment to be read, and for strict regimes a self-hosted or GovCloud option keeps it inside your own boundary.
Overreliance / automation bias OWASP LLM09 · NIST MANAGE
Each proposal leads with its evidence and a confidence so the reviewer engages with the basis; high-impact steps require explicit confirmation; and accept / edit / override rates are monitored so rubber-stamping is detectable.
Amazon Augmented AI (A2I) human-review workflows
The reviewer decides on the evidence, not the suggestion. Every proposal opens with its sources and confidence, and we watch edit and override rates per feature and reviewer; an unusually low rate flags possible over-trust and triggers review, rather than assuming the human caught everything.
Excessive agency OWASP LLM06
Least-privilege permissions, narrowly scoped tools, and explicit human approval before any outbound action.
AWS IAM least-privilege; Amazon A2I approval steps
The AI proposes tasks and a person executes them, so every outbound action waits on a reviewer’s explicit approval.
Bias / inconsistent outcomes NIST MEASURE (bias)
Supports disparate-impact testing across veteran cohorts via cohort-stratified evaluation and golden sets, with fairness metrics and periodic equity audits configured per deployment; rule changes are tested before they apply.
Bedrock Model Evaluation + golden sets, with Coa adding cohort stratification and equity audits; model cards; SageMaker Clarify for tabular signals
Rules are applied consistently and tested via scaled dry-run before they take effect; outcomes can be analyzed for disparate impact across cohorts and roll up by issue type, and the adjudicator decides each case. Equity is measured on outcomes, not inferred from a feature-importance chart.
Model / data drift & degradation NIST MEASURE → MANAGE
LLM output quality and drift are tracked by the evaluation harness and reviewer acceptance; tabular and feature drift by Model Monitor; regression evaluations run on every change, with alarms on degradation.
Bedrock Model Evaluation + golden sets and acceptance telemetry (LLM output); SageMaker Model Monitor for tabular/feature drift; Amazon CloudWatch alarms
Continuous monitoring runs alongside an evaluation harness on every change, and a scaled dry-run runs before any rollout reaches live work.
Supply chain / model provenance OWASP LLM03
Models come from a vetted, managed catalog with known provenance, and dependencies and artifacts are reviewed.
Amazon Bedrock (managed model catalog); SageMaker Model Registry
The model is a configuration choice drawn from vetted providers, our engineers own every line of code, and a security review runs in the pipeline on every change.
Improper output handling OWASP LLM05
Every model output is treated as untrusted: generated content is validated and never auto-executed.
Bedrock Guardrails output filters
Outputs arrive as proposals rendered as reviewable data rather than executed actions, and their citations are verified against the record.
Unbounded consumption / cost & DoS OWASP LLM10
Rate limits, per-tenant quotas, and throughput controls cap usage, and alarms fire on abnormal demand.
Amazon Bedrock throughput controls; CloudWatch alarms
Per-tenant quotas and continuous monitoring cap usage, and abnormal demand trips an alarm before it becomes an incident.
Accuracy over time
How accuracy is measured and stays high.
Six mechanisms measure accuracy — per response, per change, per release, and continuously — and flag it when it slips.
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Golden evaluation sets
Per releaseCurated cases with known-correct answers; every model or prompt change is re-scored against them. Regressions surface before release, not in production.
Watches: Correctness vs. known-good answers
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Contextual grounding + citation check
Per responseEvery answer is scored against its retrieved source, and every citation is checked against the record. Unsupported output is flagged or blocked before anyone relies on it.
Watches: Is each output supported by its cited source?
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Scaled dry-run / diff
Per changeA change runs across many cases in read-only mode and shows up as a before→after diff — you see exactly what it would do before it touches live work.
Watches: What a change would do, before it applies
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Human acceptance telemetry
ContinuousAccept, edit, and reject rates, tracked per feature and per reviewer. Rising edits flag a model that needs attention; suspiciously few flag rubber-stamping.
Watches: Accept / edit / override rates (over- and under-trust)
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Drift & quality monitoring
ContinuousInputs and outputs are watched for drift from the validated baseline, and an alarm fires the moment a metric crosses its threshold.
Watches: Drift & quality degradation vs. baseline
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Red-team / adversarial testing
Per releasePrompt injection, jailbreaks, and edge cases, thrown at the system on purpose — we find the failure modes before real inputs or bad actors do.
Watches: Prompt injection & failure modes
Standards & governance alignment
Mapping to recognized frameworks.
Each control maps to a recognized AI risk framework or to current federal guidance, so an evaluator can trace it back to the standard that calls for it.
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NIST AI Risk Management Framework AI RMF 1.0 · 2023
Our controls map to the framework’s four functions and its Generative AI Profile (NIST AI 600-1, 2024). NIST AI RMF is a voluntary framework; mapping to it shows alignment, not certification.
GovernMapMeasureManageGenAI Profile (600-1) -
OWASP Top 10 for LLM Applications 2025
Each guardrail in the matrix above maps to the specific LLM application risk it addresses, so you can see which control answers which weakness.
LLM01 Prompt InjectionLLM02 Sensitive-Info DisclosureLLM05 Improper Output HandlingLLM06 Excessive AgencyLLM09 MisinformationLLM10 Unbounded Consumption -
Federal AI policy OMB M-25-21 / M-25-22
Built to current federal guidance on agency AI use and acquisition, OMB M-25-21 and M-25-22 (both Apr 3, 2025), under EO 14179. Whether a deployment satisfies it is the agency CAIO’s determination.
Chief AI OfficerHigh-impact AI practicesHuman oversight & appealsModel & data portability -
VA Trustworthy AI Framework 2023
Aligns with VA’s six trustworthy-AI principles. Claims processing is a named VA AI operational area, and VA has adopted OMB’s high-impact-AI definition.
PurposefulEffective & SafeSecure & PrivateFair & EquitableTransparent & ExplainableAccountable & Monitored
Mapping to OMB M-25-21’s minimum practices for high-impact AI
OMB M-25-21 §4(b) defines minimum practices for high-impact AI, a category that adjudication decisions can fall into, and our controls map to each one. Whether a given deployment is high-impact, and whether it satisfies the memo, remains the agency CAIO’s determination.