FortiLayer

FortiLayer^™streamlines AI security and development
by enabling efficient fine-tuning, detecting layer-specific issues, and
automating security testing to safeguard AI models and reputation.

Emerging security threats and compliance requirements for AI models increase the challenges of implementing them in critical systems. Gain assurance that your model is secure and compliant with ObjectSecurity’s FortiLayer.

A monitor featuring the dashboard view of of ObjectSecurity's AI/ML Trust Analysis

ObjectSecurity™ FortiLayer

Automate deep security analysis of AI models to identify vulnerabilities and mitigate risks
Optimize fine-tuning of models by enhancing accuracy and training speed, reducing time to deployment
Catalog AI systems throughout development and deployment
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 emerging formats like CycloneDX and SPDX AI Bill of Materials (BOM)

Automated Model Assessments

Rigorous performance, sensitivity, and robustness testing of AI/ML models
Enable rapid patching of models to improve accuracy and mitigate security issues efficiently
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

A monitor featuring ObjectSecurity's AI/ML Model Assessment results

Analyses

Accuracy

Determines with standardized metrics how well an AI system achieves its intended accuracy, efficiency, speed, and resource usage

Bottlenecks

Identifies specific points within an AI system that may limit its overall performance and increase latency

Robustness

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

Explainability

Provides insight into the decision-making and underlying behavior of the AI system, including saliency maps, overfitting analysis, and weak neuron analysis

Intellectual Property

Identifies the copyrights and licenses associated with source code, data, and models to ensure compliance

Vulnerability

Detects CVEs associated with versions of AI libraries used to preprocess, train, and deploy models

A monitor displaying examples from ObjectSecurity's AI/ML compliance mapping results

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

AITRUST SBIR

NIST AISIC Member