ASI01 (Agent Goal Hijack) + ASI02 (Tool Misuse and Exploitation) + ASI03 (Identity and Privilege Abuse)

Reliability domain

Implement AI disclosure mechanisms + E.17 transparency policy
preferred label: Implement AI disclosure mechanisms

AI system operation and monitoring

Document regulatory compliance
preferred label: Document regulatory compliance + E.12 Quality management system

Model-level privacy
type: Concept
broader: Privacy & Confidentiality
definition: Risks from AI models leaking or exposing private information through their outputs or structure.
in scheme: Eticas AI Risk Taxonomy

Adversarial Threats & Attacks
type: Concept
broader: Security & Misuse
definition: The risks that arise from adversarial inputs, injected instructions, bypassed safety controls, or corrupted training data.
in scheme: Eticas AI Risk Taxonomy

Representational harm
type: Concept
broader: Bias & Fairness
definition: Risks related to how the system represents groups in its outputs — through stereotypes, toxic content, skewed representation, or homogenization. These are harms in the form of meaning and content rather than allocation of resources or opportunities.
in scheme: Eticas AI Risk Taxonomy
narrow match: §2.6 Harmful Bias and Homogenization
narrow match: Harmful bias (representational dimension)
narrow match: Non-Representative Sampling Bias
narrow match: Output → Toxicity / Harmful output

Communication & disclosure
type: Concept
broader: Transparency & Explainability
definition: Risks from inadequate communication about system capabilities, limitations, and AI involvement.
in scheme: Eticas AI Risk Taxonomy

IEEE 7001-2021 (whole standard)

Environmental Impacts

Bias & Fairness
type: Concept
alternative label: Fairness
alternative label: Bias & Discrimination
alternative label: Algorithmic fairness
broad match: Human rights, rule of law, fairness & privacy
broad match: Legal & Rights-Related Risks (3.1.D)
definition: The risk that an AI system produces outcomes that systematically advantage or disadvantage individuals or groups based on protected or sensitive attributes, leading to unequal treatment, reduced accuracy, or unjust impacts. This includes biases introduced through data, model design, or deployment context, and covers both measurable disparities and perceived unfairness in decision-making.
in scheme: Eticas AI Risk Taxonomy
narrow match: A.7.4 — Data for AI systems
narrower: Outcome disparities
narrower: Representational harm
narrower: Dynamic & Systemic bias
related match: C.3 + C.4 + C.6 — safeguards to prevent harmful and vulnerable outputs
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: rights & ethics
scope: ALL

Dangerous, Violent or Hateful Content + CBRN + Obscene/Abusive

Explainable & Interpretable

Information for users / affected parties

Malicious Actors
preferred label: Malicious Actors & Misuse

AI system verification and validation

Article 10(5) — special categories of personal data + Recital 47 (fundamental rights including privacy)

Privacy & Confidentiality
type: Concept
alternative label: Privacy
alternative label: Data Privacy
broad match: Human rights, rule of law, fairness & privacy
broad match: Personal Data Handling (DPV core)
broad match: Legal & Rights → Privacy (Unauthorized Privacy Violations × Sensitive Data types)
definition: The risk that an AI system collects, processes, or infers personal information in ways that infringe on individuals' rights to control their data (privacy), or that sensitive information is exposed, accessed, or shared without authorization (confidentiality). This includes risks from data leakage, re-identification, unauthorized use, or insufficient safeguards.
in scheme: Eticas AI Risk Taxonomy
narrower: Data collection & use
narrower: Data protection
narrower: Model-level privacy
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: rights & ethics
scope: ALL

Explainability Risk

Clauses 7 & 9 — Data Representation; Evaluation

Inclusive growth, sustainable development & environmental sustainability

AI system documentation (system documentation maintained throughout lifecycle)

Article 9 — Transparency and oversight

ai:TransparencyRisk + ai:ExplainabilityRisk (DPV AI extension)

Assign accountability

Accountable & Transparent

Data protection
type: Concept
broader: Privacy & Confidentiality
definition: Risks related to safeguards for stored and processed personal data.
in scheme: Eticas AI Risk Taxonomy

Discrimination & Toxicity

Article 11 — Technical documentation

Article 13 — Transparency and provision of information
preferred label: Article 13 — Transparency and provision of information to deployers

Annex III high-risk uses (employment, education, essential services)

Secure & Resilient
preferred label: Secure & Resilient (adversarial ML)

Output → Explainability + Non-technical → Transparency

Article 72 — post-market monitoring

Governance
type: Concept
alternative label: Accountability
alternative label: Governance & Accountability
definition: The risk that an AI system lacks adequate structures, policies, or accountability mechanisms to oversee its design, deployment, and use. Weak governance can lead to unclear responsibilities, poor documentation, limited auditability, and failure to align with legal, ethical, or organizational standards.
in scheme: Eticas AI Risk Taxonomy
narrower: Accountability
narrower: Documentation & auditability
narrower: Compliance & process
related match: Value Chain & Component Integration
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: governance & compliance
scope: ALL

Transparency & explainability
preferred label: Transparency and explainability

Data Privacy

Article 11 — Privacy and personal data protection

Fair with Harmful Bias Managed + Map 5.1 + Measure 2.11

Output → Explainability

Unequal performance across groups

Inference → Adversarial robustness + Non-technical → Misuse

AI Bias

Data Privacy

Security & Misuse
type: Concept
alternative label: Safety, Security & Misuse
alternative label: Security
alternative label: Safety
broad match: Robustness, security & safety
broad match: Security Attack
broad match: Article 12 — Reliability, safe innovation (security dimension)
definition: The risk that an AI system is exposed to AI-specific vulnerabilities, attacks, or misuse that compromise its integrity, availability, or confidentiality. This covers risks beyond traditional IT security, including adversarial inputs, prompt injection, model extraction, jailbreaking, and supply-chain risks specific to AI components. It is intended to complement, not replace, standard IT security assessments.
in scheme: Eticas AI Risk Taxonomy
narrower: Adversarial Threats & Attacks
narrower: Infrastructure & System security
narrower: Harmful & Malicious Misuse
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: technical soundness
scope: ALL

IEEE 7002-2022 (whole standard)

Dynamic & Systemic bias
type: Concept
broader: Bias & Fairness
definition: Risks related to how bias is amplified or perpetuated over time through interaction between AI systems and the social systems they operate in.
in scheme: Eticas AI Risk Taxonomy
narrow match: §2.6 Harmful Bias and Homogenization
related match: Monitor AI risk categories
related match: Governance failure

A.3 Internal organization (roles & responsibilities)

Documentation & auditability
type: Concept
broader: Governance
definition: Risks from incomplete, inaccessible, or absent documentation and audit trails.
in scheme: Eticas AI Risk Taxonomy

Non-technical → Governance dimension

Implement customer data policies

Society domain (cyber misuse + catastrophic misuse)

§2.6 Harmful Bias and Homogenization
preferred label: Harmful Bias & Homogenization
preferred label: Harmful Bias & Homogenization (representational dimension)
preferred label: Harmful Bias & Homogenization (systemic dimension)

Article 15 — accuracy, robustness and cybersecurity

Inference → Robustness → Adversarial robustness

Security domain (and Society domain F for misuse)
preferred label: Security domain

Article 10(5) + Article 6(2) + Annex III — bias-relevant data governance and high-risk classification

Output → Fairness + Training data → Accuracy + Fairness

Discrimination & Bias (Discriminatory Activities)

Unfair discrimination & misrepresentation

AI system impact assessment
preferred label: AI system impact assessment + Annex C objectives

A.8 Information for interested parties + A.6.2.8 documentation

Eticas AI Risk Taxonomy
type: ConceptScheme
description: A unified AI risk taxonomy for use across Eticas audit methodologies, assessment frameworks, and reporting outputs.
language: en
license: https://creativecommons.org/licenses/by/4.0/
publisher: Eticas
has top concept: Bias & Fairness
has top concept: Privacy & Confidentiality
has top concept: Reliability
has top concept: Governance
has top concept: Security & Misuse
has top concept: Environmental Impact
has top concept: Transparency & Explainability
has top concept: Agentic AI

Output bias (Fairness dimension)

Article 9 — risk management throughout lifecycle

Valid & Reliable

Infrastructure & System security
type: Concept
broader: Security & Misuse
definition: The risks that arise from unauthorised access, vulnerable third-party components, or over-disclosure of technical details that enable targeting. Unlike Governance risks, this includes whether the system and its environment can be technically compromised.
in scheme: Eticas AI Risk Taxonomy

§3.3.1(b) Social and Environmental Risks — energy, electricity, land, and water consumption from AI computing

Compliance & process
type: Concept
broader: Governance
definition: Risks from gaps in organisational processes for ongoing oversight, compliance, and lifecycle management of AI systems — including regulatory compliance, change management, data governance, monitoring and evaluation, remediation of failures, and incident response.
in scheme: Eticas AI Risk Taxonomy
narrow match: Value Chain & Component Integration

Environmental Impact
type: Concept
alternative label: Sustainability
alternative label: Environmental sustainability
definition: The risk that an AI system's development, deployment, or use causes negative environmental effects, such as excessive energy or water consumption, carbon emissions, or unsustainable use of hardware and resources. This includes hidden impacts across the system's lifecycle, from model training to long-term operation.
in scheme: Eticas AI Risk Taxonomy
related match: Article 95 — Codes of conduct for sustainability + Recital 139
related match: AI system impact assessment
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: technical soundness
scope: ALL

Personal Data Handling

Accountability
type: Concept
broad match: GOVERN function (entire)
broader: Governance
definition: Risks from unclear allocation of responsibility, weak human oversight, and inadequate attribution mechanisms for AI system decisions and their adverse impacts.
in scheme: Eticas AI Risk Taxonomy

Information Integrity

Privacy-Enhanced

System & Operational Risks (L1)
preferred label: System & Operational Risks (Security + Operational Misuses)

§3.2.2(a) Information content risks — malicious manipulation generating illegal/harmful content

AG-MP.3 (Multi-Agent Topology Risk) + AG-MP.2 (Agent Action-Consequence Analysis) + AG-GV.2 (Delegation Accountability)

Autonomous Actions and Tool Use
type: Concept
broader: Agentic AI
definition: Risks from the agent taking real-world actions through external tools, APIs, and services, including being granted excessive permissions, executing unnecessary or out-of-scope actions, bypassing human oversight, or consuming disproportionate resources.
in scheme: Eticas AI Risk Taxonomy

dpv:OrganisationalMeasure (DPV core)

GOVERN 1 — documented policies and processes
preferred label: GOVERN 1 — Policies, processes, procedures

Data collection & use
type: Concept
broader: Privacy & Confidentiality
definition: Risks from how personal data is collected, processed, and repurposed.
in scheme: Eticas AI Risk Taxonomy
related match: Article 5 — Prohibited AI practices (emotion recognition, biometric categorization)

Accountability — lifecycle bias monitoring
preferred label: Accountability

A.9 Use of AI systems + clause 10 (improvement / nonconformity)

GOVERN function (entire)
preferred label: GOVERN function

Article 15(5) — cybersecurity (resilience against attacks)
preferred label: Article 15(5) — cybersecurity (Recital 76 enumerates poisoning, evasion, confidentiality attacks)

Lack of transparency or interpretability

Non-technical → Misuse + Inference → Security

Output Integrity and Robustness
type: Concept
broader: Reliability
definition: Risks including hallucinations, output inconsistency, model drift, and out-of-distribution failures that undermine the dependability and trustworthiness of system outputs.
in scheme: Eticas AI Risk Taxonomy

Article 8 — Accountability and responsibility

Information Security

Accountability domain

A.2 Policies + A.3 Internal organization

Article 17 — Quality management system + Article 9 — Risk management

Output → Drift + Non-technical → Societal impact

Safe (trustworthy characteristic)

Input → Data privacy + Output → Privacy violations

Prevent hallucinated outputs

Explainable & Interpretable

Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
preferred label: Compromise of privacy by obtaining, leaking or inferring sensitive information

Explainability
type: Concept
broader: Transparency & Explainability
definition: Risks from stakeholders being unable to understand why the system produces specific outputs.
in scheme: Eticas AI Risk Taxonomy

Risk Management

Outcome disparities
type: Concept
broad match: §2.6 Harmful Bias and Homogenization
broad match: Data Bias
broader: Bias & Fairness
definition: Risks related to unequal treatment, allocation, or performance outcomes across demographic and protected groups. These are observable differences in what the system does to or for different groups.
in scheme: Eticas AI Risk Taxonomy
narrow match: Prevent customer-defined high-risk outputs

Manage 2.3 — allocation harms and disparate performance

Membership Inference Attack

Agentic AI
type: Concept
alternative label: AI Agents
alternative label: Agentic risks
definition: Risks arising from AI systems that autonomously plan, reason, and act across multiple steps using tools, memory, and external services, beyond single-inference interactions. These risks are distinct from standard LLM risks because the agent takes real-world actions, operates across time, and interacts with external systems and other agents.
in scheme: Eticas AI Risk Taxonomy
narrower: Autonomous Actions and Tool Use
narrower: Multi-agent Integrity
note: Initial scope deliberately limited to primary enterprise risks of agentic deployment. Adversarial threats specific to agents (e.g., memory poisoning) are not yet included — they sit closer to security and will be revisited as the category matures. Loss of meaningful human control over agentic systems is currently covered under Autonomy & Agency / System autonomy and may be promoted or cross-referenced here in a future iteration. Some overlap with Autonomy & Agency / System autonomy is acknowledged and will be reconciled in a future minor version.
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: post-processing
maturity: established
perspective: technical soundness
scope: LLM

Confabulation

Environmental harm

Lack of capability or robustness

Multi-agent Integrity
type: Concept
broader: Agentic AI
definition: Risks from systems composed of multiple agents — including trust exploitation, cascading failures, goal misalignment between agents, and insufficient traceability across the pipeline.
in scheme: Eticas AI Risk Taxonomy

Output → Robustness + Inference → Accuracy
preferred label: Output → Robustness

Transparency & Explainability
type: Concept
alternative label: Explainability & Transparency
alternative label: Transparency and Explainability
definition: The risk that stakeholders cannot understand how an AI system works, what it does, or why it produces specific outputs. Lack of transparency undermines informed consent, impedes oversight, and erodes trust.
in scheme: Eticas AI Risk Taxonomy
narrower: Explainability
narrower: Communication & disclosure
note: In the RAIA Guide, this category is assessed only at the Post-Processing stage.
related match: Information Integrity (provenance) + Value Chain
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: post-processing
maturity: established
perspective: governance & compliance
scope: ALL

New and amplified risks of agentic AI — Explainability, Robustness, Computational Inefficiency

Value Chain & Component Integration
preferred label: Value Chain and Component Integration

Reliability
type: Concept
alternative label: Validity and Reliability
broad match: Misinformation
broad match: Robustness, security & safety
broad match: System & Operational Risks (L1)
broad match: Article 4(b) — Reliability (general obligation)
definition: Risks arising from an AI systems failure to perform dependably — whether through degraded output integrity and robustness (e.g., hallucinations, model drift), or through inability to maintain function under adverse or changing conditions (e.g., infrastructure failure, connectivity loss).
in scheme: Eticas AI Risk Taxonomy
narrower: Output Integrity and Robustness
top concept of: Eticas AI Risk Taxonomy
lifecycle stage: pre-processing
lifecycle stage: in-processing
lifecycle stage: post-processing
maturity: established
perspective: technical soundness
scope: ALL

Societal Risks → Manipulation, Deception, Political Usage

Information Integrity (provenance) + Value Chain
preferred label: Information Integrity (provenance disclosure)

Harmful & Malicious Misuse
type: Concept
broader: Security & Misuse
definition: Risks from deliberate misuse of the AI system to generate harmful content, manipulate users, or operate beyond its intended purpose. These risks involve explicit malicious intent (human or otherwise) — distinguishing them from unintentional system failures (which belong under Reliability).
in scheme: Eticas AI Risk Taxonomy

AI system security vulnerabilities and attacks

Systemic bias under harmful bias

Privacy considerations for AI systems

Information Security

§3.1.2(a) Data risks — collection without consent + §3.1.2(d) Data risks — PII leaks

Data & Privacy domain

Non-technical → Societal impact → Environmental impact

Article 5(1)(a) — prohibited manipulative practices

AI system deployment (secure deployment)

§3.1.1(b) Model and algorithm risks (bias/discrimination) + §3.3.2(a) Ethical risks (systemic social discrimination)

Article 10 — Equality and non-discrimination

Third-party testing of adversarial robustness

Output → Harmful output + Non-technical → Misuse

Automation Bias

IEEE 2894-2024 (whole guide)