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Can You Trust the Output of Your Unprotected AI?

Build Confidence with Security for AI

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What Problems Do We Solve?

Lack of Visibility

Lack of Visibility

Organizations struggle with monitoring AI/ML pipelines for security risks.

Misconfigurations

Misconfigurations

Applications, Models, Workloads and environment often lack proper security controls.

AI Model Vulnerabilities

AI Model Vulnerabilities

AI models face threats like adversarial attacks, data poisoning, and unauthorized access.

Data Security Risks

Data Security Risks

Sensitive data can be exposed during AI model training and inference.

Compliance Challenges

Compliance Challenges

Adhering to industry and regulatory standards is quite complex.

Threat Vectors

  • 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

Data Security

  • Detecting PII/PHI exposure.
  • Prevents dataset tampering.
  • Prevents unauthorized access.

Automated Red Teaming

Automated Red Teaming

  • Dynamically tests AI models for vulnerabilities.
  • Automated adversarial attack simulation to proactively identify weaknesses.

LLM Prompt Firewall

LLM Prompt Firewall

  • Protects against prompt injection attacks.
  • Ensure safe and controlled interactions in LLM-based applications.

Training Pipeline Security

Training Pipeline Security

  • Secures model training pipelines and artifacts.
  • Safeguards trained AI models from theft, tampering, or malicious alterations.

Application Security

Application Security

  • Provides real-time protection for AI workloads.
  • Monitors for threats and anomalies.
What Problem Do We Solve

Deployment Models

On prem

On-prem (VMs, Bare metal)

Air-gapped infrastructure

Air-gapped infrastructure

Hosted Public & Private Cloud

Hosted Public & Private Cloud

AccuKnox’s hosted SaaS

AccuKnox’s hosted SaaS

Deployment Models

Unique Differentiation

Automated Red Teaming

Automated Red Teaming

Proactively stress-tests AI models, workloads using adversarial simulations.

LLM Prompt Firewall

LLM Prompt Firewall

Safeguards AI-driven chat solutions from prompt-based exploits.

Zero Trust Security

Zero Trust Security

Verifies every AI component, minimizing attack surfaces.

Comprehensive Coverage

Comprehensive Coverage

Secures the full AI lifecycle (data, training, model, application).

Compliance Automation

Compliance Automation

Ensures regulatory adherence with automated checks.

Runtime Threat Detection

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)
AI-SPM tickAI Platform security pipeline security tick cross cross
Application Security tickModels Security Runtime security yellow-tick(Only Supply chain) tickModels, not datasets tickModels, not datasets
Workload Security tickContainer Security Runtime security tick cross cross
Safety Guardrails tickSession abuse (by users) Unsafe content (to users) cross(only PANW) tick cross
Security Monitoring tickAttack detection Incident response cross(only PANW) tick cross

User Experience

A comprehensive LLM/ML lifecycle security

  • Dashboard

  • Inventory View (List)

  • Inventory View (Graph)

  • Pipelines (Graph)

  • Risk (Graph)

  • Summary

  • Model Summary

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Backed by Leading Cybersecurity Investors

mdsv CapitalnationalgridAvanta venturesDreamitDolby Familyz5-capital