Expert definitions and deep-dive guides on AI governance, security, and compliance. Built for CISOs, compliance officers, and IT leaders navigating enterprise AI adoption.
Click any term for a comprehensive guide with practical examples, regulatory context, and actionable frameworks.
AI governance is the framework of policies, processes, and controls that organizations use to ensure artificial intelligence is deployed responsibly, securely, and in compliance with regulations.
Read guideShadow AI refers to the use of artificial intelligence tools and services by employees without the knowledge, approval, or oversight of an organization's IT or security teams.
Read guideAI DLP (Data Loss Prevention for AI) is a security control that monitors, detects, and prevents sensitive data - including PII, PHI, financial data, and intellectual property - from being exposed through AI tools and large language model interactions.
Read guidePrompt injection is a security attack where malicious instructions are embedded in user inputs to manipulate a large language model into ignoring its original instructions, bypassing safety controls, or producing unauthorized outputs.
Read guideAn AI firewall is a security layer that sits between users and AI models, inspecting and filtering prompts and responses in real-time to enforce security policies, prevent data leakage, and block prompt injection attacks.
Read guideAI compliance is the practice of ensuring that artificial intelligence systems meet the legal, regulatory, and ethical requirements set by applicable laws and industry standards across all jurisdictions where an organization operates.
Read guideAlgorithmic discrimination occurs when an AI system produces outputs that unfairly disadvantage individuals or groups based on protected characteristics such as race, gender, age, or disability, often due to biased training data or flawed model design.
Read guideAI risk management is the systematic process of identifying, assessing, mitigating, and monitoring risks associated with the development, deployment, and use of artificial intelligence systems throughout their lifecycle.
Read guideAI transparency is the principle that organizations deploying AI systems must be open about how those systems work, what data they use, how decisions are made, and when users are interacting with AI rather than a human.
Read guideAutomated decision-making (ADM) is the process of making decisions about individuals using algorithms or AI systems with limited or no human involvement, particularly decisions that significantly affect rights, opportunities, or access to services.
Read guideAn AI audit is a structured evaluation of an AI system's compliance with regulatory requirements, organizational policies, ethical standards, and technical performance benchmarks, typically conducted by independent assessors.
Read guideResponsible AI is an approach to developing, deploying, and operating artificial intelligence systems that prioritizes fairness, transparency, accountability, privacy, safety, and human oversight throughout the AI lifecycle.
Read guideAI bias testing is the process of systematically evaluating AI systems for discriminatory patterns in their outputs, using statistical methods to detect disparate impact across protected groups before and after deployment.
Read guideAI observability is the practice of gaining comprehensive visibility into how AI systems are being used across an organization, what data flows through them, and whether they are performing as expected - enabling governance, cost control, and risk management at scale.
Read guideAI compliance automation is the use of technology to continuously and automatically enforce, monitor, and evidence an organization's adherence to AI-related laws, regulations, and standards, replacing manual checklists and periodic audits with real-time, machine-driven compliance controls.
Read guideAn AI policy engine is an automated system that defines, enforces, and monitors organizational rules governing how AI tools are used, what data can be processed, which models are accessible, and what outputs are permitted - replacing manual policy enforcement with real-time, programmatic controls.
Read guideAn AI control plane is the centralized management layer that governs policies, access, data protection, compliance, and observability across all AI usage in an organization - separating the management of AI from the execution of AI interactions.
Read guideAdversarial robustness is the ability of an AI system to maintain correct, safe, and predictable behavior when subjected to deliberately crafted adversarial inputs designed to cause misclassification, policy bypass, data leakage, or other unintended outcomes.
Read guideModel drift is the degradation of an AI model's performance over time as the statistical properties of real-world data diverge from the data the model was trained on, causing predictions and outputs to become less accurate, less relevant, or potentially unsafe.
Read guideData poisoning is an adversarial attack in which an attacker deliberately corrupts the training, fine-tuning, or retrieval data used by an AI system, embedding malicious patterns that cause the model to produce incorrect, biased, or harmful outputs when triggered by specific inputs.
Read guideDifferential privacy is a mathematical framework that provides provable guarantees about the privacy of individuals in a dataset by adding carefully calibrated noise to data queries, model training, or outputs - ensuring that no single individual's data can be identified or reconstructed from the results.
Read guideAI red teaming is the practice of systematically probing AI systems through adversarial testing - simulating real-world attacks, misuse scenarios, and edge cases - to identify vulnerabilities, safety failures, and governance gaps before they can be exploited in production.
Read guideModel cards are standardized documentation artifacts that describe an AI model's intended use, performance characteristics, training data, limitations, ethical considerations, and evaluation results - providing transparency and accountability for anyone who develops, deploys, or is affected by the model.
Read guideFederated learning security encompasses the techniques, protocols, and governance practices that protect distributed machine learning systems - where models are trained across multiple decentralized devices or organizations without centralizing raw data - from adversarial attacks, privacy leakage, model poisoning, and inference threats.
Read guideAI supply chain security is the practice of identifying, assessing, and mitigating risks across the entire chain of third-party dependencies that enterprise AI systems rely on - including pre-trained models, training datasets, open-source libraries, model hosting providers, data annotation services, and plugin ecosystems.
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