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Understand key AI governance terms and concepts with straightforward definitions to help you navigate the Enzai platform.
The automated or manual mechanisms that ensure AI operations adhere to organizational policies, regulatory rules, and ethical guidelines.
Ongoing observation of AI system behavior and environment after release to detect degradation, drift, or compliance breaches.
AI-driven monitoring and analysis to forecast component or system failures, ensuring operational resilience and risk mitigation in critical environments.
A structured analysis to identify and mitigate privacy risks associated with AI systems, covering data collection, use, sharing, and retention.
An approach that embeds data protection and user privacy considerations into AI system architecture and processes from the outset.
Use of AI and workflow tools to streamline governance, compliance checks, and risk mitigation activities, reducing manual effort and error.
The subjective review of AI system behaviors, decisions, and documentation by experts to identify ethical, legal, or reputational concerns not captured quantitatively.
The systematic processes and checks to ensure AI models and data pipelines meet defined standards for accuracy, reliability, and ethical compliance.
The ongoing verification of AI outputs and processes against benchmarks and test cases to catch defects, bias incidents, or policy violations.
A data-driven evaluation of potential AI threats, estimating likelihoods and impacts numerically to prioritize mitigation efforts.
The emerging computational paradigm that leverages quantum mechanics, posing new governance challenges around security, standardization, and risk.
The practice of recording AI system inputs and user queries to enable audit trails, detect misuse, and support accountability.
Techniques and policies to protect sensitive information in user queries, ensuring that logged inputs do not compromise personal or proprietary data.
A structured set of governance-focused questions used during design, procurement, or deployment to ensure AI systems align with policy requirements.
The minimum number of governance committee members required to be present to make official decisions on AI risk, policy approvals, or audit outcomes.
The controls and limits placed on AI resource usage (e.g., API calls, compute time) to enforce governance policies and prevent runaway costs or abuse.
A proactive testing approach where internal or external experts simulate attacks or misuse scenarios to uncover vulnerabilities in AI systems.
Ensuring AI systems adhere to applicable laws, regulations, and industry standards (e.g., GDPR, FDA, financial oversight) throughout their operation.
The capacity to consistently regenerate AI model results using the same data, code, and configurations, ensuring transparency and auditability.
A tool (e.g., RACI) that clarifies roles and accountabilities for each governance activity—who’s Responsible, Accountable, Consulted, and Informed.
The practice of designing, developing, and deploying AI systems in ways that are ethical, transparent, and accountable to stakeholders and society.
The process of identifying, analyzing, and prioritizing potential harms or failures in AI systems to determine appropriate mitigation strategies.
A structured set of guidelines and processes for systematically addressing AI risks across the system lifecycle, from design through retirement.
The ability of an AI system to maintain reliable performance under a variety of challenging or adversarial conditions.
A structured investigation to determine the underlying reasons for AI system failures or unexpected behaviors, guiding corrective actions.
Defined rules and controls that specify approved contexts, users, and purposes for AI system operation to prevent misuse.
Integrating security controls and best practices into AI systems from the earliest design phases to prevent vulnerabilities and data breaches.
A structured evaluation of how an AI system affects social, economic, and cultural aspects of communities, identifying potential harms and benefits.
The end-to-end process (requirements, design, build, test, deploy, monitor) for AI applications, incorporating governance and compliance checks at each stage.
The process of involving affected parties (e.g., users, regulators, impacted communities) in AI development and oversight to ensure diverse perspectives and buy-in.
The threat that AI systems may be exploited for invasive monitoring of individuals or groups, infringing on privacy and civil liberties.
Artificially generated datasets that mimic real data distributions, used to augment training sets while protecting privacy.
The systematic process of evaluating AI models against benchmarks, edge cases, and stress conditions to ensure they meet performance, safety, and compliance criteria.
The exposure arising from reliance on external data providers, model vendors, or service platforms that may introduce compliance or security vulnerabilities.
Defining boundaries or cut-off values in AI decision rules (e.g., confidence scores) to balance risks like false positives versus false negatives.
The ability to track and document each step in the AI lifecycle—from data collection through model development to deployment—to support auditing and forensics.
The curated collection of labeled or unlabeled data used to teach an AI model the relationships and patterns it must learn to perform its task.
A technique where a model developed for one task is adapted for a related task, reducing development time but requiring governance of inherited biases.
The practice of making AI system processes, decision logic, and data usage clear and understandable to stakeholders for accountability.
AI systems designed and operated in a manner that is ethical, reliable, safe, and aligned with human values and societal norms.
A modeling issue where an AI system is too simple to capture underlying data patterns, resulting in poor performance on both training and new data.
Ensuring consistent application of policies, controls, and standards across all AI systems to avoid governance gaps or uneven risk management.
A machine learning approach where models identify patterns or groupings in unlabeled data without explicit outcome guidance.
Continuous tracking of AI system availability and performance to detect outages or degradation that could impact critical operations or compliance obligations.
The practice of defining, approving, and monitoring specific AI use cases to ensure each aligns with organizational policies, ethical standards, and risk appetite.
The process of obtaining and recording explicit permission from individuals before collecting, processing, or using their personal data in AI systems.
The process of confirming that an AI model performs accurately and reliably on intended tasks and meets defined performance criteria.
Tracking fluctuations in AI model outputs or performance metrics over time to detect drift and infer potential degradation or risk.
Assessing and monitoring third-party suppliers of AI components or services to identify and mitigate potential compliance, security, or ethical risks.
The practice of managing and tracking changes to AI code, models, and datasets over time to ensure reproducibility and auditability.
The formal right held by a governance body or stakeholder to block or require changes to AI deployments that pose unacceptable risks.
Continuous surveillance of AI behavior and external signals (e.g., regulatory updates) to promptly identify and respond to emerging risks or non-compliance.
The governance processes specific to computer vision systems, ensuring data quality, bias checks, and transparency in image/video-based decision-making.
Identifying, analyzing, and prioritizing security weaknesses in AI infrastructure and applications to guide remediation efforts.
Independent runtime checks that observe AI decisions and trigger alerts or interventions when policies or thresholds are violated.
Examining model weights and structures for anomalies, backdoors, or biases that could indicate tampering or unintended behaviors.
Assessing AI systems with full knowledge of internal workings (code, parameters, architecture) to verify correctness, security, and compliance.
Governance rule defining allowed (whitelist) and disallowed (blacklist) inputs, features, or operations to enforce compliance and prevent misuse.
Allowing only pre-approved data sources, libraries, or model components in AI pipelines to reduce risk from unvetted or malicious elements.
Automating and sequencing AI lifecycle tasks (data ingestion, training, validation, deployment) to enforce governance policies and ensure consistency.
Separating AI compute environments (e.g., dev, test, prod) and data domains to limit blast radius of failures or security breaches.
Evaluating the most extreme potential failures or abuses of an AI system to inform robust risk mitigation and contingency planning.
Immutable storage ensuring logs, audit trails, and model artifacts cannot be altered once written, supporting non-repudiation and forensic review.
A model validation technique (often abbreviated “X-Val”) that partitions data into folds to rigorously assess model generalization and detect overfitting.
Techniques and methods that make an AI model’s decision process transparent and understandable to humans, supporting accountability and compliance.
A structured approach or set of guidelines that organizations use to implement, measure, and govern explainability practices across their AI systems.
Quantitative or qualitative measures (e.g., feature importance scores, explanation fidelity) used to assess the quality and reliability of AI explanations.
A set of signature-based detection patterns used to scan AI pipelines and artifacts for known malicious code or tampering.
An annual evaluation of AI governance processes, policies, and systems to ensure continued alignment with regulations and internal standards.
A governance principle aiming for no errors or policy violations in AI outputs, supported by rigorous testing, monitoring, and continuous improvement cycles.
A previously unknown security flaw in AI software or infrastructure that can be exploited before a patch or mitigation is available.
A model capability to correctly handle tasks or classify data it was never explicitly trained on by leveraging generalized knowledge representations.
A network or data governance approach that divides resources into zones with distinct policies, restricting AI system access according to data sensitivity.