Trusted by Leading Enterprises for AI/ML/LLM Risk Management & Security

Providing solutions and expertise that assess, measure, and track AI/ML/LLM model risks and vulnerabilities to improve real-world performance of models and effectively manage risk exposure.

PRODUCT SOLUTIONS ↓

Model Stress Test

Synthetically generate both naturally occurring and malicious adversarial samples to probe the boundaries of performance competence to surface and mitigate vulnerabilities including data poisoning, model evasion and model extraction. Testing competence boundaries, where models are brittle and vulnerable to attack, will ensure more robust, safer, and better performing models.
Model Risk Audit

Independently verify model performance by validating fundamental data science best practices that may adversely affect models during inference. Evaluate and document residual risks that are surfaced across the key tenets of Responsible AI including security, privacy, bias, explainability and robustness - providing a clear path to risk mitigation and better performing models.
AI Firewall

Go beyond data-drift monitoring to protect model-specific vulnerabilities revealed during stress testing; rules are dynamically configured on a model-by-model basis to detect naturally occurring and malicious inputs targeting specific model weaknesses.
Large Language Models (LLMs)

TrojAI is leveraging its expertise with expanded protections to include large language models. These protections include stress testing, input/output filtering, hallucination and bias detection, copyright monitoring, and other security events and privacy violations support.

Solutions for Enterprise Stakeholders →

AI/ML Cybersecurity

Surface and mitigate cybersecurity vulnerabilities including data poisoning, model evasion and model extraction. The threat surface is large and constantly evolving. Platform and tooling enables security teams to identify, track, and remediate cybersecurity risks.
Model Risk Management

Quantify inherent and residual risk across model portfolios, supporting model risk management frameworks and governance best practices. Introduce systematic risk management assessments across your organization to support production gating, as part of the Secure Model Development Lifecycle (SMDLC).
Data Science

Build better, safer, more robust models by probing the boundaries of model performance competence, beyond traditional accuracy metrics. Address inherent limitations of AI with systematic stress testing of unpredictable edge cases. Accelerate development of production ready models with actionable insights.
Executive & Board

Know your financial and reputational risk exposure. Understand AI portfolio risk profile across the organization over time. Prepare for regulatory compliance, ESG alignment, and audit defense. As AI becomes core to competitive advantage, model risk management solutions are required to achieve Responsible AI deployments.

AI VULNERABILITIES

_Data

AI data is vulnerable to attacks and deficiencies, such as data poisoning by malicious actors or data quality issues introduced during training which can adversely affect model performance.

_Inference

AI models can be exploited by both naturally occurring and malicious inputs that can produce incorrect outcomes at inference, and leak sensitive data, presenting security and privacy risks.

_Models

AI models have inherent deficiencies due to unpredictable long-tailed edge cases and are susceptible to issues of security, privacy, robustness, bias, and explainability, increasing financial and reputational risk exposure.