AI Security Risk Assessment
Best practices and guidance to secure AI systems
Our focus is on AI risk assessment, providing organizations with the tools and insights needed to secure their machine learning systems.

Industry Unpreparedness
We leverage insights from Microsoft's survey of 28 businesses, which found that most organizations lack the necessary tools to effectively secure their machine learning (ML) systems.

Comprehensive Perspective
Our AI Risk Assessment covers the entire AI system lifecycle in production settings.

Threat Outline
Enumerates threats at each step of AI system building and provides security guidelines.

Risk Assessment Framework
Enables organizations to assess current AI security state and track progress.
Severity, Likelihood, and Impact

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1

Severity
Based on AI model use case and data sensitivity

2

Likelihood
Depends on attack surface and available techniques

3

Impact
Related to effects on the organization
Organizations should categorize risks using a severity matrix, considering attack types like extraction, evasion, inference, inversion, and poisoning.
Data Collection and Processing
Data Sources
Collect data from trusted sources. Maintain and update a list of trusted sources. Require management approval for untrusted data sources.
Data Storage
Store data according to classification. Implement asset management and access control policies. Ensure proper security measures for sensitive AI use cases.
Data Processing
Secure processing pipelines. Track data through its entire lifecycle. Ensure compliance with existing requirements. Review and approve subsets of data used for model building.
Model Training and Selection

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Model Design
Review model training code. Conduct model design and research in appropriate environments. Document model metadata and track through development.

2

Model Training
Mimic natural drift and adversarial conditions in training. Augment datasets with common corruptions. Use adversarial retraining for robustness.

3

Model Selection
Use K-fold cross-validation to prevent overfitting. Verify performance on disparate holdout sets. Implement explicit or implicit model regularization.

4

Model Versioning
Assign new versions to retrained models. Use qualifiers to distinguish between development and production models.
Model Deployment and Monitoring
Security Testing
Define formal acceptance testing criteria. Implement automated tools for testing. Ensure test environment resembles production.
Compliance Review
Configure gateway devices to filter traffic. Document and address regulatory requirements. Implement secure configuration guidelines.
System Monitoring
Implement consistent logging across all AI systems. Regularly review event and security logs. Generate and review consolidated reports on system activity.
Incident Management
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Incident Reporting
Follow formal process to report AI system incidents, including service loss, equipment loss, and security breaches.
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Response Procedures
Develop formal incident response and escalation procedures. Document actions taken upon receiving security event reports.
3
Testing and Metrics
Periodically test incident response procedures. Track response metrics to improve processes.
Business Continuity Planning
Asset Identification
Identify and inventory critical AI assets to ensure comprehensive coverage in continuity planning.
Risk Assessment
Identify and prioritize risks associated with the impact of losing critical AI systems to attacks.
Continuity Testing
Implement a repeated schedule for business continuity testing of critical AI systems.
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