Cyber Security For Artificial Intelligence

Secure your AI and ML models against the evolving landscape of cyber threats.

Join our waitlist for exclusive early access to the platform.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Education

Learn how to attack and defend your Artificial Intelligence and Machine Learning models.

Offensive

Attack AI and machine learning models with adversary attacks. Hack chatGPT applications, machine learning models, neural networks, and more.

Defensive

Defend AI and ML models from hackers. Protect the attack surface with a shield designed to detect hackers targeting your AI and ML environment.

Firewall For AI

Protect your AI applications with AI Firewall

Real time protection, automatically optimized to detect attacks specific to each model.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Vulnerability Scanner For AI

Scan your AI applications for Exploits & Vulnerabilities

Continuous scans that automatically detect security flaws and issues in each model
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Join our waitlist for exclusive early access to the platform.

Check - Elements Webflow Library - BRIX Templates
Thanks for joining our newsletter
Oops! Something went wrong while submitting the form.
#1 AI Security Platform

AI Securty Platform

Attack or Defend your AI models.

Asset Inventory

Streamline AI and ML asset management across platforms like Vertex AI, SageMaker, DataRobot, and Hugging Face. Our software delivers unified tracking, ensuring efficiency and visibility in one powerful tool.

Asset Lifecycle Management
Real Time Visibility
Cross-Platform Integration
Security Focused Tracking

Vulnerability Scanner

Niche Vulnerability Scanner crafted to identify vulnerabilities and exploits within your AI and ML models and applications. Automate your adversarial attacks.

+100 Attacks
Blackbox or Whitebox
Automated
Adversary Machine Learning & LLM Attacks

Firewall

Designed to keep your AI and ML technologies safe by blocking and detecting threats. Guarding against attacks like prompt injection, model extraction, model evasion, jail breaks and more

LLM Shield(ChatGPT)
Adversary detection
SIEM Integrations
5 min install

Audit Logs

Monitor who is accessing your AI and ML infrastructure. Monitor & log access to notebooks, datasets, models, databases and more.

GCP,AWS, Azure, etc
AI Infrastructure Monitoring
Governance
Automated Analysis

AI Security Compliance

AI Security Compliance Frameworks

AI and Compliance Audits

NIST AI Risk Management Framework

The NIST AI Risk Management Framework (AI RMF) is tailored for AI companies to manage unique AI-related risks effectively. It focuses on four main functions:

Govern: Establish accountability for AI systems.
Map: Identify and categorize AI risks.
Measure: Evaluate the severity and probability of these risks.
Manage: Strategize on mitigating or accepting risks.

This framework helps AI companies integrate risk management into their development process, promoting the creation of reliable and ethically responsible AI applications. The AI RMF Playbook provides actionable steps to implement these principles, making it practical for AI companies to adopt and adapt to their specific needs. This approach enhances both compliance and innovation, building stakeholder trust in AI technologies.

OWASP Top 10 for Large Language Model Applications

The OWASP Top 10 for Large Language Model (LLM) Applications provides crucial guidelines to secure LLMs against common vulnerabilities. Key areas of focus include:

Prompt Injection: Manipulating LLMs through crafted inputs to trigger unintended actions.
Insecure Output Handling: Failing to validate LLM outputs, risking security breaches.
Training Data Poisoning: Compromising model integrity through tampered training data.
Model Denial of Service: Overloading systems with complex inputs, degrading performance.
Supply Chain Vulnerabilities: Introducing risks through third-party components.
Sensitive Information Disclosure: Inadvertently exposing confidential data.
Insecure Plugin Design: Using plugins that process untrusted inputs without adequate controls.
Excessive Agency: Allowing LLMs too much autonomy, causing unintended actions.
Overreliance: Depending too heavily on LLMs, leading to errors and misinformation.
Model Theft: Stealing proprietary models, risking intellectual property and data security.

Addressing these vulnerabilities with thorough testing, robust data governance, and continuous monitoring enhances LLM security and maintains the integrity and trustworthiness of AI systems.

SOC2

For AI companies undergoing SOC 2 audits, conducting a security audit to test for vulnerabilities is crucial. This includes vulnerability assessments to identify weaknesses, penetration testing to evaluate the defenses against simulated attacks, and risk assessments to understand the potential impacts of these vulnerabilities. These steps are vital for reinforcing the security measures required for SOC 2 compliance, ensuring that the systems can protect sensitive data against security threats effectively.

ISO/IEC 23894

ISO/IEC 23894:2023 offers tailored guidance for managing risks in AI systems, adapting the principles of ISO 31000:2018 to the unique challenges of AI. It ensures AI applications are robust, secure, and trustworthy. We can assist your organization in effectively integrating these risk management standards throughout the AI lifecycle, enhancing both safety and efficacy​.

EU AI Act

Under the EU AI Act, security testing for AI systems, especially high-risk ones, involves ensuring compliance through several key steps:

Risk Assessment: Identifying and addressing potential security threats to the AI system.
Technical Documentation: Maintaining records that demonstrate the system’s compliance with security requirements.
Conformity Assessment: Verifying through formal evaluations that the AI systems meet the Act’s stringent standards before deployment.
Continuous Monitoring: Regularly re-evaluating the AI system to manage new and evolving security threats.
Data Governance: Ensuring the secure and ethical handling of data used by the AI system.

These measures are aimed at maintaining high standards of security, privacy, and data integrity, enhancing the trustworthiness of AI applications within the EU.