Protect AI

Protect AI is a comprehensive AI security platform designed to safeguard artificial intelligence and machine learning systems across all stages, from model selection to runtime. It offers a suite of products operating on a unified platform to ensure end-to-end security.

Key Features:

• Advanced Threat Detection and Mitigation: Utilizes sophisticated algorithms to identify and neutralize potential threats.
• Model Monitoring: Continuously tracks AI model performance and behavior for proactive defense.
• Robust Data Encryption: Secures sensitive data both at rest and in transit.

Use Cases:

• Securing Large Language Models (LLMs): Protects complex AI systems from vulnerabilities.
• Enterprise Security: Integrates with existing systems to enhance overall security posture.
• Research and Development: Supports data scientists and ML engineers in securing AI/ML environments.

Benefits:

• Enhanced Security Posture: Provides comprehensive protection against emerging threats.
• Proactive Defense: Offers early vulnerability detection and alert systems.
• Scalability and Flexibility: Adapts to various environments and evolving security needs.

• Scans ML models for unsafe code across multiple formats (H5, Pickle, SavedModel, PyTorch, TensorFlow, ONNX, Keras, Scikit-learn, DMLC XGBoost).
• Protects against model serialization attacks, including credential theft, data theft, data poisoning, and model poisoning.
• Detects, redacts, and sanitizes LLM prompts and responses in real-time.
• Provides defense against data leakage, adversarial attacks, and content moderation.
• Anonymizes PII and redacts secrets within AI systems.
• Counteracts threats such as prompt injections and jailbreaks.
• Supports deployment on any LLM (GPT, Llama, Mistral, Falcon) and LLM framework (Azure OpenAI, Bedrock, Langchain).
• Offers cost-effective CPU inference for LLM security.
• Provides advanced Regex analysis and URL reachability for precise LLM security.
• Stops AI threats at runtime with deep visibility and control.
• Offers 27 turnkey policies based on 15 different security scanners.
• Tracks the entire conversation flow, including tools, function calls, downstream workflows, multi-turn attacks, and metadata.
• Provides actionable insights for security teams to identify, analyze, investigate, and remediate violations and trends at scale.
• Integrates with security tools like DataDog, Splunk, Elastic, and PagerDuty.
• Detects attack patterns and integrates with custom scanners.
• Monitors tool and function calls, retrievals, and embeddings.
• Aligns with industry standards like NIST, MITRE, and OWASP.
• Offers flexible deployment options (eBPF or SDK).
• Automatically discovers AI apps.
• Scans 35+ different model formats (PyTorch, TensorFlow, ONNX, Keras, Pickle, GGUF, Safetensors, LLM-specific formats).
• Detects deserialization attacks, architectural backdoors, and runtime threats.
• Leverages a security research community (huntr) to stay ahead of new vulnerabilities.
• Continuously scans public models on Hugging Face.
• Provides flexible policies customizable for first- and third-party models.
• Offers granular security rules for model metadata, approved formats, verified sources, and security findings.
• Integrates into ML pipelines and DevOps workflows via CLI, SDK, or Local Scanner.
• Supports model sources like Hugging Face, MLFlow, S3, and SageMaker.
• Runs directly in CI/CD pipelines as a lightweight Docker container.
• Provides a centralized audit trail of all evaluations.
• Tests AI apps across multiple threat vectors with an attack library of 450+ known attacks.
• Uses trained LLMs as detectors to ensure accuracy.
• Creates relevant attacks leveraging business objectives, base models, deployed guardrails, RAG pipelines, and system prompts.
• Allows red teamers to set attack goals using Natural Language (no code necessary).
• Produces in-depth, conversation-level visibility for risk analysis and remediation.
• Enables users to upload custom attack prompt sets.
• Maps vulnerabilities to standard frameworks such as OWASP Top 10 for LLMs and DASF.
• Exports results to CSV and JSON for collaboration.