ENTERPRISE GRID

Assess your AI system's risk before it's too late.

ARIA provides a structured, intelligent evaluation framework covering governance, security, fairness, and compliance — with a downloadable audit-ready report.

RISK DOMAINS

65+

CONTROL CHECKS

SCORING LAYERS

01PROFILE

02USE CASE

03DATA

04MODEL

05SECURITY

06FAIRNESS

07GOVERNANCE

08OPS

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Application Profile

ESTABLISH ASSESSMENT BOUNDARY — PROVIDE CORE SYSTEM IDENTIFICATION DETAILS

Application / Product Name *

Organization / Client Name *

AI Function / Purpose *

Model Type / Architecture *

Deployment Environment

Primary Jurisdiction / Geography

DEPLOYMENT SCALE

Estimated Users / Decisions Affected Monthly

Deployment Phase

Brief Description of the System *

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Use Case & Impact Assessment

DOMAIN 1 OF 7 — BUSINESS CRITICALITY · DECISION IMPACT · AUTONOMY LEVEL

What is the degree of decision automation?

Fully human-in-the-loop — AI only provides suggestions; humans make all decisions

Human-on-the-loop — AI decides, human monitors and can override in real time

Mixed — AI automates routine decisions; humans review edge cases

Mostly automated — humans review only escalated or high-risk decisions

Fully autonomous — AI makes all decisions with no human review

What is the potential harm if the AI makes a wrong decision?

Minimal — inconvenience or minor financial impact, easily reversible

Low — moderate financial/service impact, correctable within days

Medium — significant financial, reputational, or operational harm

High — legal rights, employment, healthcare, or credit decisions impacted

Critical — safety of life, severe financial ruin, or irreversible rights violations

Is there a clear, accessible appeals or override process for affected individuals?

Yes — documented, accessible appeals process with SLA commitments

Yes — process exists but may not be well-documented or communicated

Partial — internal override exists but not available to affected parties

Limited — ad-hoc process only, no formal mechanism

None — no override or appeals mechanism exists

Financial exposure from a single significant AI failure (estimate)

Under $10K — minimal financial risk

$10K – $500K — moderate financial risk

$500K – $5M — significant financial risk

$5M – $50M — high financial risk

Over $50M — critical financial risk

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Data Governance

DOMAIN 2 OF 7 — DATA QUALITY · CONSENT · LINEAGE · SENSITIVITY

What types of data does the system process? (Select all that apply)

Personally Identifiable Information (PII) — name, email, ID numbers

Sensitive Personal Data — health, biometric, ethnicity, religion, political views

Financial Data — banking, credit, transaction records

Behavioral / Usage Data — browsing, location, activity logs

Third-Party / Purchased Data — external data providers or brokers

Publicly Available Data only — open datasets, web scraping

Synthetic / Anonymized Data

What is the state of data consent and legal basis?

Full compliance — explicit consent obtained, legal basis documented for all data uses

Mostly compliant — consent for primary use cases, minor gaps in edge cases

Partial — consent for some data, legitimate interest claimed for others without full assessment

Limited — consent not systematically obtained; relying on implied consent

Unknown / None — legal basis not established or data consent not tracked

How would you rate the overall data quality (completeness, accuracy, freshness)?

VERY POORPOORMODERATEGOODEXCELLENT

Is data lineage and provenance tracked end-to-end?

Full lineage — automated tracking from source to model; documented in data catalog

Mostly tracked — lineage documented for critical datasets; gaps in derived features

Partial — some documentation but incomplete or manually maintained

Minimal — source known, but transformation history not fully captured

None — no lineage tracking; origin of training data unknown for some portions

Are data retention and deletion policies enforced?

Yes — automated enforcement with audit logs and right-to-erasure capabilities

Partial — policies defined but enforcement is manual or inconsistent

No — no defined retention policies or enforcement mechanisms in place

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Model Risk & Performance

DOMAIN 3 OF 7 — ACCURACY · DRIFT · EXPLAINABILITY · LIFECYCLE

How well is model performance characterized and documented?

Comprehensive — precision, recall, F1, AUC, calibration tested across multiple subgroups

Good — standard metrics tracked, some subgroup analysis performed

Moderate — basic accuracy metrics only, limited subgroup testing

Limited — performance tested on overall dataset only, no failure mode analysis

Unknown — model performance not formally evaluated or documented

Is model drift monitored in production?

Yes — automated drift detection with alerting and retraining triggers defined

Yes — periodic manual evaluation (monthly or quarterly)

Partial — some monitoring but no formal drift thresholds or response process

No — model deployed and not monitored for performance degradation

Is the model explainable to end users and regulators?

Fully explainable — SHAP/LIME or intrinsic explainability; plain-language explanations available

Partially explainable — technical explanations available; end-user explanations limited

Black box with post-hoc tools — SHAP applied but not integrated into decisions

Minimal — general description only; no feature-level explanations

None — completely opaque model; cannot explain outputs

Is there a model registry and version control?

Yes — formal registry with versioning, metadata, test results, and rollback capability

Yes — version control exists; limited metadata and test result documentation

Partial — files versioned in source control but no formal model registry

No — no version control; current model state unknown or undocumented

If it is an LLM/generative AI, is hallucination risk assessed and mitigated?

N/A — not a generative AI system

Yes — RAG or grounding used; output validation and human review in place

Partial — some guardrails in place but not comprehensive

No — hallucination risk unaddressed in a generative AI system

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Security & Adversarial Resilience

DOMAIN 4 OF 7 — INJECTION · POISONING · ACCESS CONTROL · SUPPLY CHAIN

Has the system been tested against adversarial attacks (prompt injection, evasion, poisoning)?

Yes — red teaming, adversarial robustness testing, and penetration testing completed

Partial — some adversarial testing done; not comprehensive across all threat vectors

Limited — standard security testing only, no AI-specific adversarial testing

No — no adversarial or AI-specific security testing performed

How is access to the model and training data controlled?

Strong — RBAC/ABAC, MFA, least-privilege enforced; all access logged and reviewed

Good — role-based access with authentication; logging in place but reviews are infrequent

Moderate — basic access controls; some privileged access without formal controls

Weak — shared credentials or overly broad access permissions

None — no formal access control; model and data openly accessible internally

Are AI supply chain dependencies (third-party models, libraries, APIs) managed?

Yes — AI Bill of Materials (AI-BOM) maintained; vendor risk assessments completed

Partial — dependency inventory exists; vendor assessments incomplete

Limited — general software inventory but no AI-specific dependency tracking

None — third-party AI dependencies untracked or unvetted

Are model artifacts (weights, checkpoints) protected against tampering?

Yes — cryptographic signing, hash verification, and integrity checks on deployment

Partial — access controls in place but no formal integrity verification

No — model artifacts stored without integrity protection mechanisms

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Fairness, Ethics & Transparency

DOMAIN 5 OF 7 — BIAS · DISPARATE IMPACT · CONTESTABILITY · EXPLAINABILITY

Has the system been tested for bias across protected groups (gender, race, age, disability)?

Comprehensive — demographic parity, equal opportunity, and disparate impact tested across all relevant groups

Partial — some protected groups tested; methodology documented

Limited — high-level fairness review only; no statistical bias testing

None — no bias or fairness testing conducted

How is transparency ensured for users and affected individuals?

High — users notified AI is used; explanations provided; model card publicly available

Moderate — users notified of AI use; limited explanation of how decisions are made

Low — AI use disclosed in terms/policy but not prominently communicated

None — users are unaware that AI is making decisions that affect them

Does the system include ethical review and responsible AI governance?

Yes — formal AI Ethics Committee review; Responsible AI policy applied; documented

Partial — ethical considerations reviewed informally; no formal committee

Minimal — ethics mentioned in design docs but no formal review process

None — no ethical review or responsible AI framework applied

Is there a harmful content or output risk for this system?

No risk — system cannot generate harmful content; output is structured/constrained data

Low risk — minimal potential for harm; guardrails in place and tested

Medium — some potential; content filtering or output validation implemented

High — significant risk; partial safeguards only (e.g., generative AI with gaps)

Critical — high risk with no content moderation or output controls

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Compliance & Governance

DOMAIN 6 OF 7 — REGULATORY MAPPING · DOCUMENTATION · APPROVALS · AUDIT TRAIL

Which regulatory frameworks apply? (Select all that apply)

EU AI Act — High-Risk or General Purpose AI provisions

GDPR / UK GDPR — data protection and automated decision-making (Art. 22)

NIST AI RMF — US AI Risk Management Framework

ISO/IEC 42001 — AI Management System Standard

Financial Regulation — FCRA, ECOA, Basel/SR 11-7, FCA, MAS

Healthcare Regulation — HIPAA, MDR, FDA SaMD guidance

CCPA / US State Privacy Laws

None identified / Not yet assessed

How complete is the compliance documentation?

Complete — requirements mapped to controls; evidence pack with model cards, data sheets, approvals

Mostly complete — major documents present; minor gaps in evidence freshness

Partial — some documentation; requirement-to-control mapping incomplete

Limited — minimal documentation; no formal compliance mapping

None — no compliance documentation exists

Is there a formal approval and sign-off process before deployment?

Yes — multi-stakeholder approval (legal, risk, compliance, data, business) with documented sign-off

Yes — approval process exists but not all relevant stakeholders are included

Informal — approvals happen via email or verbal confirmation; not formally recorded

None — no formal approval gate before AI system deployment

Is third-party vendor AI risk managed?

N/A — no third-party AI vendors or models used

Yes — vendor AI risk assessments completed; contractual AI provisions in place

Partial — some vendor reviews; no formal AI-specific contractual requirements

No — third-party AI risks not assessed or managed

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Operations & Monitoring

DOMAIN 7 OF 7 — INCIDENT RESPONSE · FALLBACK · RETRAINING · KPI TRACKING

Is there a defined AI-specific incident response plan?

Yes — AI incident response playbook with defined roles, escalation paths, and SLAs

Partial — general IT incident response adapted for AI; no AI-specific playbook

Limited — informal incident handling; no documented AI incident process

None — no incident response process for AI failures

Is there a fallback or fail-safe mechanism when the AI fails or underperforms?

Yes — tested fallback to rule-based system or human review with automatic triggering

Yes — fallback exists but triggering is manual or not regularly tested

Partial — degraded service mode planned but not implemented

None — no fallback; AI failure causes full service outage

Is there a governed retraining and model update process?

Yes — defined triggers, approval gates, regression testing, and rollback on retraining

Yes — retraining process documented; limited regression testing coverage

Ad-hoc — retraining happens reactively without formal governance

None — no retraining process; model not updated after initial deployment

Is real-time monitoring and alerting in place for the AI system?

Yes — comprehensive monitoring covering performance, data, system health, and business KPIs

Good — technical monitoring in place; limited business/model-specific KPI monitoring

Basic — infrastructure monitoring only; no AI-specific metric tracking

None — no monitoring or alerting in place

Is there any additional context, known risks, or mitigating circumstances you want considered?

ASSESSMENT COMPLETE — ARIA EVALUATION REPORT

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OVERALL VERDICT

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RESIDUAL RISK SCORE

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0255075100

SCORING BREAKDOWN

DOMAIN RISK SCORES

◆ ARIA ASSESSMENT NARRATIVE

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📌 PRIORITY REMEDIATION ACTIONS

#	ACTION	DOMAIN	PRIORITY	TIMELINE

Assessment Complete — Download Audit-Ready Report

Comprehensive PDF report with all scores, findings, and recommendations for regulator submission

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