Consistency
Use common terminology, control objectives, quality models, and testing practices across teams.
Standards & Frameworks
Turn standards into operating practices and evidence
AiQualTest is built around recognized AI governance, quality, testing, security, and software engineering guidance—helping organizations translate expectations into controls, evaluations, decisions, and evidence.
Why standards matter
A shared language for quality, risk, and accountability
Standards help leaders define expectations, practitioners implement repeatable controls, and reviewers understand the evidence. AiQualTest connects those layers without claiming certification or endorsement.
Traceability
Connect risks and requirements to policies, evaluations, approvals, results, and remediation.
Defensibility
Preserve reviewable evidence showing what was tested, who decided, and why a release proceeded.
Supported references
Frameworks we help organizations implement and apply
Our methodologies map to recognized references; applicability and implementation scope depend on each organization, system, jurisdiction, and engagement.
AI Governance & Responsible AI
Frameworks for governing AI risk, accountability, management systems, and regulatory readiness.
NIST AI Risk Management Framework (AI RMF)
Govern, Map, Measure, and Manage AI risks across the lifecycle.
How AiQualTest supports it: Maps policies, risk assessments, evaluations, approvals, and evidence to AI RMF outcomes.
ISO/IEC 42001 (AI Management Systems)
Management-system practices for responsible development and use of AI.
How AiQualTest supports it: Supports control mapping, ownership, operating procedures, evaluation records, and audit preparation.
ISO/IEC 23894 (AI Risk Management)
Guidance for identifying, assessing, treating, and monitoring AI risk.
How AiQualTest supports it: Connects risk scenarios to tests, mitigations, residual-risk decisions, and traceable evidence.
EU AI Act (where applicable)
Risk-based regulatory obligations for AI systems placed on or used in the EU.
How AiQualTest supports it: Helps teams map applicable obligations to controls, validation activities, human oversight, and evidence.
Software Quality & Testing
Quality models and testing practices for repeatable, defensible software and AI-system validation.
ISO/IEC/IEEE 29119 (Software Testing)
Processes, documentation, and techniques for structured software testing.
How AiQualTest supports it: Supports test strategy, scenario design, execution records, traceability, and test reporting.
ISO/IEC 25010 (Software Product Quality)
A quality model spanning functional suitability, reliability, security, and more.
How AiQualTest supports it: Maps quality characteristics to measurable evaluation criteria, thresholds, and release decisions.
ISTQB best practices
Widely adopted testing vocabulary, techniques, and lifecycle practices.
How AiQualTest supports it: Reinforces risk-based testing, test design, defect analysis, and practitioner learning.
IEEE software engineering guidance
Relevant software engineering guidance for lifecycle quality and assurance.
How AiQualTest supports it: Supports disciplined requirements, verification, validation, configuration, and evidence practices.
Security & Privacy
Application and AI-security practices for threat-informed design, testing, and continuous improvement.
OWASP Top 10
Common and consequential web-application security risks.
How AiQualTest supports it: Provides test patterns and evidence for application-security controls around AI-enabled systems.
OWASP Top 10 for LLM Applications
Security risks specific to LLM applications, agents, prompts, data, and tools.
How AiQualTest supports it: Supports adversarial scenarios for prompt injection, data exposure, unsafe output, and excessive agency.
OWASP ASVS
Verifiable application-security requirements and assurance levels.
How AiQualTest supports it: Maps security requirements to validation scenarios, results, exceptions, and audit-ready evidence.
OWASP SAMM
A maturity model for improving software-security practices.
How AiQualTest supports it: Helps assess current maturity, prioritize improvements, and track program evidence over time.
References indicate alignment and implementation support. They do not state or imply certification, accreditation, approval, or endorsement by any standards organization.
How AiQualTest supports adoption
From practitioner capability to audit-ready evidence
Standards become useful when people understand them, controls operationalize them, and validation produces evidence.
Learning
Role-based learning paths explain the intent, terminology, and practical application of supported standards.
Consulting
Advisors help translate framework expectations into operating models, controls, policies, and roadmaps.
Assessment
Readiness and maturity assessments identify gaps, prioritize risk, and establish an implementation baseline.
Validation
Scenario-driven evaluations test whether AI and software controls behave as intended before release.
Audit-ready evidence
AiQT Labs preserves policies, results, approvals, decisions, and traceability for review and audit preparation.
Consulting services
Adopt standards without turning them into a paperwork exercise
AiQualTest consulting helps teams select applicable references, assess maturity, define operating models, map controls, build evaluation programs, and establish evidence practices.
- 01
Scope
Identify systems, stakeholders, risks, obligations, and applicable standards.
- 02
Assess
Baseline current controls, testing practices, documentation, and evidence gaps.
- 03
Implement
Define policies, ownership, workflows, scenarios, thresholds, and release gates.
- 04
Validate
Evaluate control effectiveness, preserve evidence, and prioritize continuous improvement.
AiQT Labs
Policies, evaluations, and evidence mapped in one assurance workflow
AiQT Labs helps teams map framework expectations to policy controls, link controls to evaluation scenarios, record human approvals, and retain audit-ready results and decision history.
- Standard or framework
- Control objective
- Policy
- Evaluation
- Human decision
- Evidence
Learning paths
Prepare practitioners to apply—not merely recognize—standards
Role-based learning connects framework concepts to risk analysis, test design, governance workflows, evidence review, and implementation decisions.
Governance leaders
Operating models, accountability, policy design, risk treatment, and oversight.
Quality engineers
Risk-based testing, quality models, scenario design, metrics, and release criteria.
Auditors and reviewers
Traceability, evidence sufficiency, exceptions, approvals, and residual risk.
Ready for an enterprise pilot?
See how AiQT Labs evaluates AI applications, enforces governance, and produces audit-ready evidence in your environment.