Building Guardrails in AI Systems with Threat Modeling

Executive Summary: Safeguarding AI Innovation

The rapid expansion of AI necessitates robust security and privacy guardrails. Our research synthesizes 14 diverse threat modeling frameworks into a unified library of 63 controls, refined by expert feedback. This provides a practical, self-service tool for developers to integrate threat analysis across the AI development lifecycle, ensuring safer, more reliable AI systems.

Schedule Your Strategy Session

Key Metrics & Impact

Our comprehensive approach translates complex research into actionable insights, driving tangible security improvements for AI/ML deployments.

14 Frameworks Synthesized

63 Unified Controls

10 Expert Consultations

Discuss Your Implementation

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

AI/ML Application Type

How the type of AI/ML application (e.g., persistent ML model, continuous learning, user data interaction) influences threat applicability.

Categorization by Type

Threats vary significantly based on whether an application involves continuous learning, handles user data, or is a static, persistent model.

80% Agreement among coders for initial threat classification

Customized Questionnaires

A 'piece-wise approach' allows applications to customize their threat assessment based on their specific characteristics, avoiding irrelevant questions.

AI/ML Component/Stage

Understanding where threats emerge within the AI/ML lifecycle: data, model, artefact, or system/infrastructure.

Component-Specific Threats

Threats can be grouped by the application component they affect (data, model, artefact, system/infrastructure) for targeted mitigation.

Enterprise Process Flow

Conduct Security Review

→

Is it an AI/ML-based Application?

→

Review with Baseline Requirements

→

Is Application Continuously Trained?

→

Review with Additional-Continuous-Learning

→

Is there user data or interaction involved?

→

Review with Additional-User-Data

→

Report Questionnaire to Threat Modeling Team

Phased Assessment

Aligning questions with chronological developmental phases ensures early identification and mitigation of risks.

Language & Definition

The importance of clear, simple, and uniformly defined terminology for threat modeling questions.

Simplified Language

Using binary, simple questions with clear definitions makes threat modeling accessible to non-ML experts on the product team.

Feature	Traditional Frameworks	GuardRails Library
Terminology	Inconsistent, complex	Uniform, clear definitions
Accessibility	Requires ML expertise	Accessible to all developers
Actionability	Abstract threats	Specific, actionable questions

Enhanced Clarity

Adding descriptions to clarify ambiguous questions, like 'data drift,' improves comprehension.

Specificity of Threats

Distinguishing between general security issues and AI/ML-specific threats, and the appropriate level of detail.

AI/ML-Specific Scope

Focusing on threats uniquely applicable to AI/ML systems, while allowing for the addition of emerging threats.

Exclusion of Broad Threats

General security questions not specific to AI/ML are excluded to maintain focus.

Testing Complexity

Differentiating between threats developers can assess manually and those requiring automated adversarial testing.

Manual vs. Automated Testing

Questions are designed for manual developer assessment, reserving complex adversarial testing for red teams.

Addressing CVE-2019-20634: ML Email Classification Subversion

A crucial example highlighting the unique security threats in AI/ML systems is CVE-2019-20634, which demonstrated how an ML-based email classification system could be subverted. This vulnerability underscored the need for specific threat modeling for AI, distinct from traditional software systems. Our GuardRails framework provides targeted questions to help identify and mitigate such AI-specific risks early in the development lifecycle, preventing system subversion and ensuring integrity. This case study emphasizes the critical role of proactive threat modeling in maintaining the reliability and security of AI applications.

Future Work

Automated adversarial testing is identified as an area for future research and development.

Calculate Your AI Safeguarding ROI

Estimate the potential annual cost savings and hours reclaimed by proactively implementing AI threat modeling and security guardrails.

Your Industry

Number of Employees (working with AI)

Avg. Hours per Week on AI/ML Ops

Avg. Hourly Rate ($)

Estimated Annual Savings $0

Annual Hours Reclaimed 0

Optimize Your AI Security Budget

Your GuardRails Implementation Roadmap

A phased approach to integrate the GuardRails threat modeling library into your AI/ML development pipeline.

Phase 1: Initial Assessment

Conduct a baseline assessment using GuardRails for all AI/ML applications.

Phase 2: Tailored Integration

Customize questionnaires based on application type (continuous learning, user data interaction).

Phase 3: Expert Review & Mitigation

Review assessment results with threat modeling team and define mitigation strategies.

Phase 4: Continuous Improvement

Regularly revisit assessments as models evolve and new threats emerge; contribute to open-source library.

Start Your GuardRails Journey

Ready to Secure Your AI Innovations?

Don't leave your AI systems vulnerable. Our experts are ready to help you implement robust threat modeling strategies.