AI Defensive Methods

Techniques for identifying inputs crafted to cause AI model misclassification or misbehavior, including perturbation analysis, input validation, certified defenses, and adversarial example detection.

2 threat patterns

Frameworks and tools for evaluating AI systems for discriminatory outcomes, including statistical parity testing, disparate impact analysis, intersectional auditing, and algorithmic accountability methodologies.

5 threat patterns

Technical approaches for detecting AI-generated phishing campaigns, including LLM-output classifiers, behavioral email analysis, AI-enhanced threat intelligence, and organizational controls.

2 threat patterns

Technical approaches for identifying text produced by large language models, including statistical classifiers, watermark detection, stylometric analysis, and their documented limitations.

2 threat patterns

Technical approaches for identifying malicious modifications to AI training data, including statistical outlier detection, provenance tracking, dataset integrity verification, and model behavior analysis.

2 threat patterns

Technical approaches for identifying AI-generated or AI-manipulated visual and audio media, including forensic analysis, neural network classifiers, and provenance verification.

2 threat patterns

Technical approaches for identifying AI-generated or cloned speech audio, including spectral analysis, liveness detection, neural network classifiers, and procedural verification.

2 threat patterns

Practices for securing the AI model supply chain, including model provenance verification, dependency scanning, trusted model registries, and third-party component validation.

2 threat patterns

Standards and techniques for establishing content authenticity and origin, including C2PA cryptographic provenance, invisible watermarking, and content authentication infrastructure.

3 threat patterns

Organizational and technical controls for preventing deepfake-enabled social engineering attacks, including verification protocols, multi-channel authorization, employee training, and incident response procedures.

2 threat patterns

Techniques for training and deploying AI models while protecting individual privacy, including differential privacy, federated learning, secure computation, and data minimization strategies.

5 threat patterns

Techniques for preventing prompt injection attacks on LLM-based applications, including input sanitization, privilege separation, instruction hierarchy enforcement, and the structural reasons why no complete solution exists.

4 threat patterns

Infrastructure for recording AI system decisions, inputs, outputs, and actions to support accountability, compliance, forensic analysis, and continuous improvement.

4 threat patterns

Enterprise platforms and methodologies for continuous monitoring of AI system behavior, including drift detection, performance degradation alerts, fairness monitoring, and risk dashboards.

4 threat patterns

Design patterns for maintaining meaningful human control over AI systems, including human-in-the-loop architectures, escalation mechanisms, override controls, and automation level frameworks.

5 threat patterns

Organizational frameworks for managing AI model lifecycles, including model registries, approval workflows, version control, access management, and decommissioning procedures.

5 threat patterns

Structured adversarial testing methodologies for evaluating AI system safety and security, including prompt injection testing, bias probing, capability elicitation, and organizational red team operations.

4 threat patterns