Olivier Reuland
Pragmatic Information Security and Privacy Expert
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Ai Security
AI Security Assessment
An assessment of AI system security and governance based on ISO/IEC 42001 standard.
AI Security Assessment
An assessment of AI system security and governance based on ISO/IEC 42001 standard.
Progress: 0 of 11 questions answered
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1
AI Governance
Do you have governance structures for AI system development and deployment?
Why this matters:
Establish clear governance to ensure AI systems are developed and used responsibly with appropriate oversight.
We have a comprehensive AI governance framework with board oversight, ethical guidelines, and regular audits.
We have basic AI policies and oversight committees.
AI governance is informal and ad-hoc.
We have no specific governance for AI systems.
Not Applicable
2
AI Governance
How do you ensure accountability for AI system decisions and outcomes?
Why this matters:
Maintain accountability to ensure AI systems can be audited and their decisions explained.
We implement comprehensive audit trails, explainability requirements, and accountability frameworks for all AI systems.
We have basic logging and documentation requirements for AI systems.
Accountability is informal and not systematically enforced.
We have no accountability mechanisms for AI systems.
Not Applicable
3
AI Governance
Do you conduct risk assessments for AI systems?
Why this matters:
Identify and mitigate risks specific to AI systems including bias, security vulnerabilities, and unintended consequences.
We conduct comprehensive AI-specific risk assessments including bias, security, and ethical considerations.
We include AI systems in general risk assessments.
AI risks are considered informally.
We do not assess risks specific to AI systems.
Not Applicable
4
AI Data Security
How do you protect training data used for AI systems?
Why this matters:
Secure training data to prevent data poisoning attacks and protect sensitive information.
Training data is encrypted, access-controlled, and protected against tampering with integrity verification.
Training data has basic access controls and encryption.
Training data protection is informal.
Training data has no specific security protections.
Not Applicable
5
AI Data Security
Do you validate and sanitize data inputs to AI systems?
Why this matters:
Prevent adversarial inputs and data poisoning that could compromise AI system behavior.
We implement comprehensive input validation, sanitization, and adversarial input detection.
We have basic input validation for AI systems.
Input validation is informal or inconsistent.
We do not validate AI system inputs.
Not Applicable
6
AI Data Security
How do you protect against data poisoning attacks?
Why this matters:
Prevent malicious alteration of training data that could compromise AI model integrity.
We implement data integrity monitoring, anomaly detection, and secure data pipelines.
We have basic monitoring of data integrity.
Data poisoning protection is informal.
We have no protection against data poisoning.
Not Applicable
7
AI Model Security
How do you protect AI models from theft and unauthorized access?
Why this matters:
Prevent model theft and unauthorized use that could compromise intellectual property and system integrity.
Models are encrypted, access-controlled, and protected with digital rights management.
Models have basic access controls and encryption.
Model protection is informal.
Models have no specific security protections.
Not Applicable
8
AI Model Security
Do you protect against adversarial attacks on AI models?
Why this matters:
Prevent adversarial inputs designed to manipulate AI model behavior and outputs.
We implement adversarial training, input preprocessing, and runtime monitoring for attacks.
We have basic protections against adversarial inputs.
Adversarial protection is informal.
We have no protection against adversarial attacks.
Not Applicable
9
AI Model Security
How do you ensure AI model integrity and prevent tampering?
Why this matters:
Maintain model integrity to ensure consistent and reliable AI system behavior.
We implement model integrity verification, secure storage, and change management procedures.
Models have basic integrity checks.
Model integrity is informally managed.
We have no model integrity protections.
Not Applicable
10
AI Supply Chain Security
How do you secure your AI development and deployment pipelines?
Why this matters:
Protect the AI supply chain from compromise that could affect model integrity and security.
We implement secure CI/CD pipelines with automated security scanning, code signing, and access controls.
Pipelines have basic security controls.
Pipeline security is informal.
Pipelines have no security measures.
Not Applicable
11
AI Supply Chain Security
Do you verify the security of third-party AI components and libraries?
Why this matters:
Ensure third-party AI components do not introduce security vulnerabilities or backdoors.
We conduct comprehensive security assessments and continuous monitoring of third-party AI components.
We perform basic security reviews of third-party components.
Third-party component security is informal.
We do not verify third-party AI component security.
Not Applicable
Please answer all 11 remaining questions.
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