ObjectSecurity™ AI/ML Trust Analysis
Catalog your risks, Fortify your models
Emerging security threats and compliance requirements for AI models increase the challenges of implementing them in critical systems.
Gain assurance that your model is safe and compliant with ObjectSecurity’s AI/ML Trust Analysis.
ObjectSecurity™ AI/ML Trust Analysis
- Catalog AI system of systems at any point in the development or deployment lifecycle
- Standardize analysis of models for performance, security, and explainability
- Assess and track risks for AI systems and their data
- Generate supporting evidence for compliance and auditing
- Configure policies and standards for trustworthy developed models
- Map supporting evidence to state and federal laws, and risk management frameworks
- Integrate into CI/CD, DevSecOps, MLOps, and other development and deployment pipelines
- Export reports to PDF, JSON, XML, CSV, HTML, and other emerging formats like CycloneDX and SPDX AI Bill of Materials (BOM)
Automated Model Assessments
- Rigorous performance, sensitivity, and robustness testing of AI/ML models
- Gain a deep understanding of model internals and behavior with layer-wise explainability analysis
- Detect security flaws and vulnerabilities in the trained model and supporting environments
- Obtain automated mitigations and suggestions for improving model accuracy and robustness
- Track and evaluate metrics after each iteration of training
Analyses
Determines with standardized metrics how well an AI system achieves its intended accuracy, efficiency, speed, and resource usage
Identifies specific points within an AI system that may limit its overall performance and increase latency
Determines how well an AI system performs under unexpected conditions or malicious changes to input data. This includes testing against added noise, edge cases, and adversarial attacks
Provides insight into the decision-making and underlying behavior of the AI system, including saliency maps, overfitting analysis, and weak neuron analysis
Identifies the copyrights and licenses associated with source code, data, and models to ensure compliance
Detects CVEs associated with versions of AI libraries used to preprocess, train, and deploy models
AI Regulations and Compliance
- Ensure compliance with AI laws and regulations
- Generate reports in preparation for emerging and future compliance
- Map to NIST Artificial Intelligence Risk Management Framework, MITRE ATLAS, and other risk management frameworks
- Track documentation and decisions made during development process
- Catalog novel analysis results for proving model accuracy and robustness
- Visualize progress of model towards being compliant
- Access the dashboard for high-impact AI risk analysis results
