The CAISO certification is an experience-gated, independently assessed, executive AI-security credential anchored in EU regulation, AI risk management, secure AI lifecycle controls, and board-level accountability. It is not a course-completion certificate. The designation is awarded only after the candidate demonstrates knowledge, judgement, and relevant professional experience.
Role Definition
The Chief Artificial Intelligence Security Officer (CAISO) is the executive or designated senior leader responsible for the security, resilience, and assurance of AI-enabled systems, AI-supported business processes, and AI governance controls across the enterprise and its supply chain.
How the CAISO Differs from Adjacent Roles
CAISO vs. CISO
The CISO manages enterprise-wide cyber risk and the security control environment. The CAISO specialises in AI-specific attack surfaces, AI lifecycle controls, AI model governance, and AI-related assurance while remaining aligned to the CISO function.
CAISO vs. CAIO / Head of AI
The CAIO drives AI adoption, innovation, and business value. The CAISO is not responsible for AI value creation; the CAISO is responsible for AI security, defensibility, resilience, and safe operationalisation.
CAISO vs. DPO / Privacy Counsel
The DPO owns personal-data compliance and the privacy programme. The CAISO must understand privacy and data governance but owns the security architecture and control assurance for AI use cases.
CAISO vs. Model Risk / Internal Audit
Internal audit provides independent validation, challenge, and assurance. The CAISO builds first- and second-line AI security controls; independent review remains a separate function.
Eligibility Requirements
The CAISO role is not entry-level. Candidates must meet the following admission criteria before proceeding to assessment.
| Requirement | Threshold | Rationale |
|---|---|---|
| Professional experience | At least 5 years in cybersecurity, privacy, risk, IT governance, AI/ML engineering, technology assurance, or a closely related field | The role requires professional judgement that cannot be acquired through coursework alone. |
| Leadership or architecture experience | At least 2 years in a lead, managerial, architectural, advisory, or governance capacity | CAISO decisions affect policy, assurance, and executive reporting. |
| AI-related exposure | Documented involvement in at least 1 AI, ML, LLM, model governance, AI risk, or AI security project | Candidates must demonstrate direct exposure to AI systems or AI assurance activity. |
| Ethics and conduct | Signed code of ethics and declaration of conflicts of interest | The role has direct trust and governance implications. |
| Optional prior credentials | CISM, CISSP, AAISM, AIGP, ISO/IEC 42001, privacy, or audit credentials may support eligibility | Prior credentials may shorten evidence review but do not substitute for CAISO-specific assessment. |
Body of Knowledge
The CAISO Body of Knowledge defines the minimum domains evaluated before the credential is awarded. Weightings are balanced between governance, regulation, technical security, operational control, and executive accountability.
| Domain | Weight | Knowledge Assessed |
|---|---|---|
| 1. AI governance, strategy and operating model | 15% | AI operating model; role design; accountability; three lines of defence; AI system inventory; policy hierarchy; AI literacy planning; alignment with enterprise security governance. |
| 2. EU regulation, privacy and compliance | 15% | EU AI Act; GDPR intersections; NIS2 implications; human oversight; transparency; sectoral obligations; records, evidence, and internal accountability; contract and vendor clauses for AI use. |
| 3. AI risk management, impact assessment and assurance | 15% | Risk identification and classification; impact assessment methods; control selection; assurance evidence; residual risk acceptance; model and use-case approval gates; internal audit readiness. |
| 4. Secure AI / ML / LLM lifecycle | 15% | Secure design; data poisoning; adversarial examples; prompt injection; jailbreaks; model inversion and extraction; model theft; evaluation and red teaming; release management; rollback criteria. |
| 5. Data security, provenance and supply chain | 10% | Training, test and inference data governance; lineage and provenance; licensing and IP issues; third-party models; open-source dependencies; retrieval corpora; confidentiality and integrity controls. |
| 6. Platform, MLOps, cloud and runtime security | 10% | Identity and access control; secrets; APIs; containers; model serving; vector databases; agentic workflows; GPU and specialised infrastructure; cloud security baselines; logging and monitoring architecture. |
| 7. Monitoring, incident response, resilience and forensics | 10% | Operational monitoring; model drift versus abuse; incident triage; containment; kill switch and rollback; business continuity; evidence preservation; reporting and post-incident review. |
| 8. Executive leadership, board reporting and third-party oversight | 10% | Board communication; KRIs and KPIs; risk appetite; investment decisions; AI supplier due diligence; control attestations; executive decision memos; cross-functional escalation and governance forums. |
Reference Standards and Frameworks
The certification scheme is anchored in the following external references.
| Reference | Why It Anchors the Scheme |
|---|---|
| EU AI Act and related Commission guidance | Defines the regulatory and governance context for providers and deployers operating in the EU. |
| NIST AI Risk Management Framework | Provides a practical structure for AI risk identification, governance, measurement, and management. |
| ISO/IEC 42001 | Provides the management-system backbone for accountable and auditable AI governance. |
| ISO/IEC 23894 | Provides an AI-specific risk management lens that complements the management-system view. |
| ISO/IEC 42005 | Strengthens the impact-assessment layer for AI systems and their downstream effects. |
| OWASP Top 10 for LLM Applications | Provides a concrete list of common LLM and GenAI attack patterns and control themes. |
| MITRE ATLAS | Supports AI threat modelling and adversary behaviour analysis. |
| NIST Secure Software Development Framework (SSDF) | Provides a secure-development baseline that can be adapted to AI and MLOps pipelines. |
Assessment Architecture
The CAISO credential is awarded only after independent assessment. Training may help candidates prepare, but attendance does not automatically entitle a candidate to the designation.
Eligibility and Ethics Review
Purpose: Confirm that the candidate has the required professional background and accepts professional conduct obligations.
Format: Document review; CV; project evidence; ethics declaration.
Criteria: Mandatory gateway — pass / fail
Proctored Knowledge Exam
Purpose: Test breadth of knowledge across governance, regulation, AI-specific threats, controls, and operations.
Format: 150 questions; 180 minutes; scenario-heavy multiple-choice and multiple-response; closed-book.
Criteria: ≥ 70% overall and ≥ 60% in any major domain cluster
Written Executive Case Study
Purpose: Test whether the candidate can translate AI security knowledge into decisions and control design.
Format: 120-minute proctored case requiring a risk register, control priorities, incident-response decisions, and a short board memo.
Criteria: ≥ 70% overall with no critical fail on legal, risk, or control judgement
Oral Defence / Assessor Panel
Purpose: Validate executive judgement, communication, and ability to defend trade-offs.
Format: 30–45 minute remote or in-person panel interview using one or two structured scenarios.
Criteria: Pass / fail — panel includes at least one security expert and one governance or legal expert
Certification principle: The certification decision is independent from training delivery. A candidate who passes the assessment without attending the issuer's training is fully eligible, provided the entry requirements are met. This separation materially increases the credential's credibility.
Practical Competence Tasks
The following tasks represent core competencies that appear in the CAISO assessment. Candidates must demonstrate the ability to perform these in realistic, scenario-based conditions.
| Task Category | What the CAISO Candidate Must Demonstrate |
|---|---|
| Regulatory classification | Classify an AI use case and identify the security and governance obligations that follow from the regulatory context. |
| Threat modelling | Produce an attack-path analysis for an LLM-based assistant or AI-enabled workflow, including prompt injection, data exfiltration, model abuse, and vendor dependencies. |
| Control architecture | Select and prioritise minimum viable controls for training, fine-tuning, inference, access control, logging, third-party models, and data provenance. |
| Vendor assurance | Evaluate an AI supplier or model provider, identify control gaps, and define contractual or assurance requirements before approval. |
| Incident management | Draft a response plan for a realistic AI incident such as model manipulation, jailbreak-driven data leakage, unsafe output, or poisoned training data. |
| Executive communication | Prepare a short board-level briefing that explains AI risk, recommended mitigations, residual risk, and required investment in business language. |
Certification Maintenance and Recertification
The CAISO designation must be maintained through continuing professional education and ongoing practice.
| Element | Requirement |
|---|---|
| Validity period | 3 years |
| Continuing professional education | 60 CPE credits over 3 years, including at least 20 in AI / ML / LLM / AI security and at least 10 in law, governance, privacy, or assurance. |
| Ethics attestation | Annual attestation to the code of ethics and disclosure of relevant sanctions, conflicts of interest, or material professional misconduct. |
| Currency requirement | Evidence of continued practice in AI, cybersecurity, governance, assurance, or related work; otherwise a refresher or reassessment route applies. |
| Scheme refresh | The body of knowledge, item bank, and case studies are reviewed at least annually because AI threats and regulation change rapidly. |
Scheme Governance
The CAISO certification programme is governed by the following principles to ensure rigour, fairness, and relevance.
- A published scheme handbook covers the body of knowledge, scoring logic, retake policy, appeals process, and recertification rules.
- Training operations are separated from the certification decision-making body.
- Exam items and case studies are refreshed regularly to keep pace with AI threats, model architectures, agentic systems, and EU regulatory updates.
- A multidisciplinary advisory board includes cybersecurity, AI engineering, privacy, legal, audit, and sector experts.
- A formal job-task analysis is conducted before each major scheme revision and revisited on a fixed cycle.
Enquiries
For questions about eligibility, the assessment process, or the certification programme, contact ask@caiso.eu.