Can You Trust the Output of Your Unprotected AI?
Build Confidence with Security for AI
What Problems Do We Solve?
Lack of Visibility
Organizations struggle with monitoring AI/ML pipelines for security risks.
Misconfigurations
Applications, Models, Workloads and environment often lack proper security controls.
AI Model Vulnerabilities
AI models face threats like adversarial attacks, data poisoning, and unauthorized access.
Data Security Risks
Sensitive data can be exposed during AI model training and inference.
Compliance Challenges
Adhering to industry and regulatory standards is quite complex.
Threat Vectors
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Sentiment Analysis
Concern: Coerce LLM into generating harmful or toxic responses.
Risk: Propagation of offensive content, damaging user trust. -
Hallucination
Concern: Provides false or misleading information.
Risk: Misleading decision-making , damage to the model's credibility. -
Prompt Injection
Concern: Manipulates prompts to bypass safeguards , generate harmful content.
Risk: Breach of trust and potential for malicious use. -
Code
Concern: Model generates malicious or obfuscated code that bypasses security measures.
Risk: Enables cyberattacks and system breaches.
Our Solution
Data Security
- Detecting PII/PHI exposure.
- Prevents dataset tampering.
- Prevents unauthorized access.
Automated Red Teaming
- Dynamically tests AI models for vulnerabilities.
- Automated adversarial attack simulation to proactively identify weaknesses.
LLM Prompt Firewall
- Protects against prompt injection attacks.
- Ensure safe and controlled interactions in LLM-based applications.
Training Pipeline Security
- Secures model training pipelines and artifacts.
- Safeguards trained AI models from theft, tampering, or malicious alterations.
Application Security
- Provides real-time protection for AI workloads.
- Monitors for threats and anomalies.
Deployment Models
On-prem (VMs, Bare metal)
Air-gapped infrastructure
Hosted Public & Private Cloud
AccuKnox’s hosted SaaS
Unique Differentiation
Automated Red Teaming
Proactively stress-tests AI models, workloads using adversarial simulations.
LLM Prompt Firewall
Safeguards AI-driven chat solutions from prompt-based exploits.
Zero Trust Security
Verifies every AI component, minimizing attack surfaces.
Comprehensive Coverage
Secures the full AI lifecycle (data, training, model, application).
Compliance Automation
Ensures regulatory adherence with automated checks.
Runtime Threat Detection
Provides continuous monitoring.
Key Differentiators
| Criteria | |
Cloud AI-SPM (Tool X) |
End-to-end security (Tool Y) |
AI red teaming (Tool Z) |
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|---|---|---|---|---|---|---|---|---|---|
| AI-SPM |  AI Platform security pipeline security |
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| Application Security | Models Security Runtime security |
 (Only Supply chain) |
Models, not datasets |
Models, not datasets |
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| Workload Security | Container Security Runtime security |
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| Safety Guardrails | Session abuse (by users) Unsafe content (to users) |
(only PANW) |
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| Security Monitoring | Attack detection Incident response |
(only PANW) |
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User Experience
A comprehensive LLM/ML lifecycle security
Dashboard
Inventory View (List)
Inventory View (Graph)
Pipelines (Graph)
Risk (Graph)
Summary
Model Summary
Available on Marketplaces
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Backed by Leading Cybersecurity Investors
