real-time model threat detection
Monitors deployed ML models for active security threats including prompt injection attacks, model poisoning attempts, and adversarial inputs. Provides continuous scanning of model inputs and behaviors to identify malicious patterns in real-time.
prompt injection attack prevention
Detects and blocks prompt injection attempts that try to override model instructions or extract sensitive information. Analyzes incoming prompts for malicious patterns and injection techniques before they reach the model.
model access control enforcement
Enforces fine-grained access controls on model deployments, restricting who can access, modify, or query models. Logs all access attempts for audit purposes.
security incident reporting
Generates detailed incident reports documenting threats detected, actions taken, and impact assessment. Provides executive summaries and technical details for different stakeholders.
model poisoning detection
Identifies attempts to corrupt model training data or inject malicious data into model retraining pipelines. Monitors data quality and detects anomalies that indicate poisoning attacks before they degrade model performance.
adversarial input detection
Identifies adversarial examples and edge-case inputs designed to fool or degrade model performance. Detects inputs that are statistically unusual or crafted to exploit model vulnerabilities.
compliance audit trail generation
Automatically generates and maintains audit logs documenting all model access, modifications, threat detections, and security incidents. Creates compliance-ready documentation for regulated industries.
model vulnerability assessment
Scans deployed models for known vulnerabilities, misconfigurations, and security weaknesses. Provides assessment reports identifying specific risks and remediation recommendations.
+4 more capabilities