Overview
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.
Capabilities
- 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.
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