AI Governance Glossary
40+ essential terms in AI safety, governance, certification, and compliance. Optimized for featured snippets and comprehensive learning.
A
AI Governance
The institutional frameworks, policies, and processes that guide the safe development, deployment, and oversight of artificial intelligence systems. CSOAI provides a comprehensive 52-article charter for global AI governance aligned with institutional oversight principles.
AI Safety
The discipline of ensuring artificial intelligence systems operate reliably, securely, and in alignment with intended objectives. Encompasses technical safety, alignment testing, robustness evaluation, and governance structures.
AI Alignment
The process of ensuring AI system objectives and behaviors align with human values, intentions, and ethical principles. A critical component of AI safety that addresses reward misalignment and specification gaming.
AI Agent
An autonomous software system that perceives its environment, makes decisions, and takes actions to achieve specified goals. In CSOAI Byzantine governance, 33 AI agents reach consensus using fault-tolerant voting mechanisms.
AI Audit
A comprehensive assessment of AI systems for compliance with governance standards, safety requirements, and regulatory frameworks. Essential for CASA certification across all three levels.
Adversarial Testing
Deliberate attempts to find vulnerabilities and weaknesses in AI systems through generated adversarial examples and attack scenarios. A key component of security assessment and red teaming.
Autonomous Weapons
AI-driven military systems capable of engaging targets without direct human intervention. Subject to heightened governance requirements and CSOAI Level 3 Defense certification protocols.
Algorithmic Accountability
The responsibility of organizations to explain, justify, and be accountable for algorithmic decisions affecting users. Requires transparent audit trails, decision logging, and human oversight mechanisms.
B
Byzantine Consensus
A distributed consensus mechanism where 33 AI agents reach agreement on decisions using Byzantine Fault Tolerance principles. Requires 22/33 (67%+) majority for approval, mathematically resistant to 33% malicious actors.
Bias Testing
Systematic evaluation of AI systems for fairness issues, discriminatory outcomes, and representation imbalances across demographic groups. Critical for ensuring equitable AI deployment.
BMCC
Byzantine Management and Consensus Coordination—a member organization of the CSOAI ecosystem responsible for training, certification, and workforce development in Byzantine governance principles.
C
CASA Certification
The Coordinated Security for AI Certification offered at three levels: Level 1 (Commercial, $5-25K), Level 2 (Government, $25-100K), Level 3 (Defense, $100-500K). Provides global recognition of AI safety and governance compliance.
CSOAI
Coordinated Security for Artificial Intelligence—the global institutional governance standard and ecosystem providing Byzantine consensus-based oversight, 52-article charter, and comprehensive AI safety framework.
CMMC
Cybersecurity Maturity Model Certification—a compliance framework increasingly applied to AI systems requiring enhanced security assessments, threat resilience, and security posture maturity across five levels.
CA3O
Coordinated AI Accountability and Oversight Organization—an oversight body within the CSOAI ecosystem ensuring institutional accountability and cross-jurisdictional governance alignment.
Compliance Framework
A structured approach to governance, policies, and processes ensuring AI systems meet regulatory requirements and institutional standards. Includes documentation, auditing, monitoring, and corrective action procedures.
D
Data Governance
The policies, procedures, and controls governing data collection, storage, processing, and use in AI systems. Ensures data quality, security, privacy, and compliance with regulations like GDPR.
Deepfake
Synthetic media created using deep learning to realistically portray false scenarios, typically video or audio. A high-risk AI application requiring verification mechanisms and authentication protocols.
Digital Twin
A virtual representation of a physical system, process, or object used for simulation, testing, and monitoring. Enables safe testing of AI systems in controlled digital environments before real-world deployment.
DSRB
Defense Safety and Responsibility Board—a member organization of the CSOAI ecosystem responsible for oversight and distribution of certified AI systems in defense and critical infrastructure sectors.
E
Ethical AI
AI systems developed and deployed according to ethical principles including fairness, transparency, accountability, and respect for human autonomy. Core requirement for CSOAI certification.
EU AI Act
The European Union's comprehensive AI regulation taking effect August 2, 2026. Requires mandatory compliance for all AI systems in EU markets with penalties up to €30M or 6% of revenue. CSOAI certification directly supports EU AI Act compliance.
Explainability
The ability to describe and justify AI system decisions in human-interpretable terms. Essential for accountability, debugging, and regulatory compliance. Requires transparent decision pathways and interpretable feature importance.
F
Fairness Testing
Systematic evaluation of AI systems to ensure equitable treatment across protected characteristics including race, gender, age, and socioeconomic status. Measures disparate impact and algorithmic bias.
Foundation Model
A large-scale AI model trained on diverse data that can be adapted for multiple downstream tasks. Examples include GPT-4, Claude, and other frontier AI models requiring governance oversight.
Frontier AI
Advanced AI systems operating at the edge of current technological capabilities, including large language models and multimodal systems. Requires highest governance standards and red teaming protocols.
G
Generative AI Governance
Specialized governance frameworks for generative AI systems like language models, image generators, and content creation tools. Addresses hallucinations, bias, copyright, and misuse prevention.
Guardrails
Technical safeguards and constraints built into AI systems to prevent unsafe outputs, enforce compliance with policies, and maintain alignment with institutional guidelines during deployment.
H
Hallucination
When AI systems, particularly language models, generate plausible-sounding but false or nonsensical information. A critical safety concern requiring verification and grounding mechanisms in governance frameworks.
High-Risk AI
AI systems with potential for significant harm, including those affecting legal rights, safety, employment, education, and critical infrastructure. Requires highest governance levels and intensive monitoring per EU AI Act.
I
Impact Assessment
Comprehensive evaluation of AI system effects on individuals, organizations, and society. Includes legal, ethical, operational, and safety impacts. Required before deployment in high-risk contexts.
ISO 42001
International standard for AI management systems providing framework for governance, risk management, and implementation. CSOAI standards align with and exceed ISO 42001 requirements.
N
NIST AI RMF
National Institute of Standards and Technology AI Risk Management Framework—U.S. government standard for AI risk assessment, mitigation, and governance. CSOAI certification supports NIST AI RMF compliance.
P
Prompt Injection
A security vulnerability where malicious instructions are inserted into AI system inputs to manipulate behavior. Requires input validation and safety filtering mechanisms in deployed systems.
Proof of AI
A blockchain-based tokenization system providing cryptographic proof of CASA certification. Enables transparent, auditable verification of AI system governance compliance on public networks.
R
Red Teaming
Adversarial security testing where specialized teams systematically attempt to find vulnerabilities, exploits, and failure modes in AI systems. Essential for CASA certification and safety validation.
Responsible AI
An approach to developing and deploying AI emphasizing ethics, accountability, transparency, fairness, and safety. Encompasses governance, oversight, and continuous improvement throughout system lifecycle.
Risk Management
Systematic identification, assessment, prioritization, and mitigation of risks in AI systems. Includes technical, organizational, and governance-level risk controls aligned with ISO 42001 and NIST frameworks.
S
Shadow AI
Unmanaged, undocumented AI systems and tools used within organizations without formal governance or oversight. Represents a significant compliance risk addressed through institutional AI governance frameworks.
Security Testing
Comprehensive evaluation of AI system security including adversarial robustness, injection vulnerabilities, data security, and infrastructure hardening. Conducted by certified security teams like AIdome.
T
Transparency
Making AI system operations, decisions, and capabilities openly visible and understandable to stakeholders. Enables accountability, builds trust, and supports informed decision-making about AI use.
Trust Score
A quantified metric indicating the governance compliance and safety level of certified AI systems. Based on audit results, testing outcomes, and ongoing monitoring within CASA certification framework.
V
Validation
Process of confirming that AI systems meet intended specifications and perform effectively in real-world conditions. Includes testing, monitoring, and continuous evaluation throughout deployment lifecycle.
Verification
Process of confirming that AI systems are built and operate according to specifications and governance requirements. Includes security assessment, compliance checking, and artifact validation.