Achieve Multi-Cloud
AI & LLM Security
Against Top Modern Attack Vectors
All Things AI Security From
Development to Deployment
Model Security
Agentless and quick setup
- Vulnerability Scanning
- Supply Chain Hardening
- Observability into prompt usage
- Model Hijacking Protection
Dataset Security
Defense against data extraction
- Data Privacy Scanning
- Secure Data Access
- Data poisoning protection
- Secure Data Pipelines
Application Security
No Jailbreaking and Prompt Injection
- AI Red Teaming
- Secure AI Packaging
- Development environment hardening (Jupyter Notebook)
- Application security testing
Container Security
Runtime Security for Containers
- AI Workload Security
- Secure AI Inference
- Securing NIM Microservices
- Container image scanning
The ModelKnox dashboard is simple and intuitive, provides real-time visibility of potential security risks: prompt injection, model architecture vulnerability, and misconfigurations that expose data breaches or policy breaches.
ModelKnox Features
Data Security
- Prevent dataset tampering
- Find secrets in datasets
- Protect dataset access
- Secure data storage
Training Security
- Prevent model backdooring
- Ensure model provenance
- Protect training pipelines
- Secure artifact access
Model Security
- Conduct AI red teaming
- Enforce safety policies
- Ensure AI compliance
- Verify supply chain
Application Security
- Package models securely
- Validate application security
- Manage security posture
- Protect AI workloads
Runtime Security
- Observe runtime security
- Ensure safe consumption
- Ensure secure inference
- Respond to incidents
Achieve multi-cloud AI Workload and LLM Security
One Platform to Secure
All AI Workloads
Siloed Tools Mean Less Context and Slower Response
- Pre-Development Model Security Scan
- Multi-Cloud Asset Discovery for AI Applications
- AI Application Security Assessment
- Automated Triage for Model Security Findings
Pre-Development Model Security Scan
Identify vulnerabilities in AI models before application development. Scan for risks like insecure architectures, data leakage, or adversarial attack susceptibility.
Use Case: Detect if a model exposes sensitive data during inference or is prone to exploitation.
Multi-Cloud Asset Discovery for AI Applications
Discover AI/LLM/ML assets across multi-cloud environments. Automatically identify models, datasets, and compute resources linked to applications.
Use Case: Locate all LLM instances and connected datasets deployed across AWS, Azure, and GCP.
AI Application Security Assessment
Detect security issues in AI applications, including cloud infrastructure and model-level risks. Find misconfigured storage, insecure APIs, or vulnerabilities like prompt injection.
Use Case: Identify exposed S3 buckets or models vulnerable to jailbreaking.
Automated Triage for Model Security Findings
Automate the handling of bulk security findings. Create rules to classify, prioritize, and resolve issues like misconfigurations or unauthorized access.
Use Case: Flag high-risk problems such as unencrypted datasets for immediate action.
Defend Against AI Attack Vectors
Jailbreaking
Prompt injection
Backdoor and data poisoning
Adversarial inputs
Insecure output handling
Data extraction and privacy
Data reconstruction
Denial of service
Watermarking and evasion
Model theft
ModelKnox Use Cases
Did you know – AI attacks are headlines every other week?
Key Differentiators
Criteria | Cloud AI-SPM (Tool X) |
End-to-end security (Tool Y) |
AI red teaming (Tool Z) |
||||||
---|---|---|---|---|---|---|---|---|---|
AI-SPM | AI Platform security pipeline security | ||||||||
Application Security | Models Security Runtime security | (Only Supply chain) | Models, not datasets | Models, not datasets | |||||
Workload Security | Container Security Runtime security | ||||||||
Safety Guardrails | Session abuse (by users) Unsafe content (to users) | (only PANW) | |||||||
Security Monitoring | Attack detection Incident response | (only PANW) | |||||||