ASI01 (Agent Goal Hijack) + ASI02 (Tool Misuse and Exploitation) + ASI03 (Identity and Privilege Abuse)
Reliability domain
Implement AI disclosure mechanisms + E.17 transparency policy
Implement AI disclosure mechanisms
AI system operation and monitoring
Document regulatory compliance
Document regulatory compliance + E.12 Quality management system
Risks from AI models leaking or exposing private information through their outputs or structure.
Model-level privacy
The risks that arise from adversarial inputs, injected instructions, bypassed safety controls, or corrupted training data.
Adversarial Threats & Attacks
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.
Representational harm
Risks from inadequate communication about system capabilities, limitations, and AI involvement.
Communication & disclosure
IEEE 7001-2021 (whole standard)
Environmental Impacts
Fairness
Bias & Discrimination
Algorithmic fairness
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.
Bias & Fairness
Security Attack
Dangerous, Violent or Hateful Content + CBRN + Obscene/Abusive
Explainable & Interpretable
Information for users / affected parties
Malicious Actors
Malicious Actors & Misuse
AI system verification and validation
Article 10(5) — special categories of personal data + Recital 47 (fundamental rights including privacy)
Prevent customer-defined high-risk outputs
Privacy
Data Privacy
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.
Privacy & Confidentiality
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
Non-Representative Sampling Bias
Accountable & Transparent
Risks related to safeguards for stored and processed personal data.
Data protection
Discrimination & Toxicity
Human rights, rule of law, fairness & privacy
Article 11 — Technical documentation
Article 13 — Transparency and provision of information
Article 13 — Transparency and provision of information to deployers
Annex III high-risk uses (employment, education, essential services)
Secure & Resilient
Secure & Resilient (adversarial ML)
Output → Explainability + Non-technical → Transparency
Article 72 — post-market monitoring
Accountability
Governance & Accountability
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.
Governance
Transparency & explainability
Transparency and explainability
Article 11 — Privacy and personal data protection
Fair with Harmful Bias Managed + Map 5.1 + Measure 2.11
Output → Explainability
Robustness, security & safety
A.7.4 — Data for AI systems
Article 95 — Codes of conduct for sustainability + Recital 139
Unequal performance across groups
Inference → Adversarial robustness + Non-technical → Misuse
Article 4(b) — Reliability (general obligation)
AI Bias
Safety, Security & Misuse
Security
Safety
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.
Security & Misuse
IEEE 7002-2022 (whole standard)
Risks related to how bias is amplified or perpetuated over time through interaction between AI systems and the social systems they operate in.
Dynamic & Systemic bias
A.3 Internal organization (roles & responsibilities)
Risks from incomplete, inaccessible, or absent documentation and audit trails.
Documentation & auditability
Non-technical → Governance dimension
Implement customer data policies
Society domain (cyber misuse + catastrophic misuse)
§2.6 Harmful Bias and Homogenization
Harmful Bias & Homogenization
Harmful Bias & Homogenization (representational dimension)
Harmful Bias & Homogenization (systemic dimension)
Article 15 — accuracy, robustness and cybersecurity
Inference → Robustness → Adversarial robustness
Security domain (and Society domain F for misuse)
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
AI system impact assessment + Annex C objectives
A.8 Information for interested parties + A.6.2.8 documentation
A unified AI risk taxonomy for use across Eticas audit methodologies, assessment frameworks, and reporting outputs.
Eticas AI Risk Taxonomy
Output bias (Fairness dimension)
Article 9 — risk management throughout lifecycle
Harmful bias (representational dimension)
Valid & Reliable
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.
Infrastructure & System security
§3.3.1(b) Social and Environmental Risks — energy, electricity, land, and water consumption from AI computing
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.
Compliance & process
Sustainability
Environmental sustainability
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.
Environmental Impact
Personal Data Handling
Risks from unclear allocation of responsibility, weak human oversight, and inadequate attribution mechanisms for AI system decisions and their adverse impacts.
Information Integrity
Privacy-Enhanced
Output → Toxicity / Harmful output
System & Operational Risks (L1)
System & Operational Risks (Security + Operational Misuses)
§3.2.2(a) Information content risks — malicious manipulation generating illegal/harmful content
Article 5 — Prohibited AI practices (emotion recognition, biometric categorization)
AG-MP.3 (Multi-Agent Topology Risk) + AG-MP.2 (Agent Action-Consequence Analysis) + AG-GV.2 (Delegation Accountability)
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.
Autonomous Actions and Tool Use
dpv:OrganisationalMeasure (DPV core)
GOVERN 1 — documented policies and processes
GOVERN 1 — Policies, processes, procedures
Risks from how personal data is collected, processed, and repurposed.
Data collection & use
Accountability — lifecycle bias monitoring
A.9 Use of AI systems + clause 10 (improvement / nonconformity)
GOVERN function (entire)
GOVERN function
Article 15(5) — cybersecurity (resilience against attacks)
Article 15(5) — cybersecurity (Recital 76 enumerates poisoning, evasion, confidentiality attacks)
Misinformation
Lack of transparency or interpretability
Non-technical → Misuse + Inference → Security
Governance failure
Risks including hallucinations, output inconsistency, model drift, and out-of-distribution failures that undermine the dependability and trustworthiness of system outputs.
Output Integrity and Robustness
Article 8 — Accountability and responsibility
C.3 + C.4 + C.6 — safeguards to prevent harmful and vulnerable outputs
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
Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
Compromise of privacy by obtaining, leaking or inferring sensitive information
Risks from stakeholders being unable to understand why the system produces specific outputs.
Explainability
Personal Data Handling (DPV core)
Risk Management
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.
Outcome disparities
Manage 2.3 — allocation harms and disparate performance
Membership Inference Attack
AI Agents
Agentic risks
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.
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.
Agentic AI
Confabulation
Environmental harm
Lack of capability or robustness
Risks from systems composed of multiple agents — including trust exploitation, cascading failures, goal misalignment between agents, and insufficient traceability across the pipeline.
Multi-agent Integrity
Legal & Rights-Related Risks (3.1.D)
Output → Robustness + Inference → Accuracy
Output → Robustness
Explainability & Transparency
Transparency and Explainability
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 the RAIA Guide, this category is assessed only at the Post-Processing stage.
Transparency & Explainability
New and amplified risks of agentic AI — Explainability, Robustness, Computational Inefficiency
Value Chain & Component Integration
Value Chain and Component Integration
Legal & Rights → Privacy (Unauthorized Privacy Violations × Sensitive Data types)
Validity and Reliability
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).
Reliability
Societal Risks → Manipulation, Deception, Political Usage
Information Integrity (provenance) + Value Chain
Information Integrity (provenance disclosure)
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).
Harmful & Malicious Misuse
Article 12 — Reliability, safe innovation (security dimension)
AI system security vulnerabilities and attacks
Systemic bias under harmful bias
Privacy considerations for AI systems
§3.1.2(a) Data risks — collection without consent + §3.1.2(d) Data risks — PII leaks
Monitor AI risk categories
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
Data Bias
Output → Harmful output + Non-technical → Misuse
Automation Bias
IEEE 2894-2024 (whole guide)