Protecting AI models with our Cyber Security for Generative AI Services

1. Model Integrity  & Security

• Model Hardening: We ensure generative AI models are hardened against adversarial attacks (e.g., adversarial perturbations) where small manipulations to input data can trick AI models into making incorrect predictions.

• Adversarial Testing: Tests to identify weaknesses in AI models using simulated adversarial attacks designed to exploit the model's vulnerabilities.

• Model Watermarking: Implementation of AI model watermarking to protect intellectual property by embedding unnoticeable watermarks that can prove ownership if the model is stolen or copied.

2. Data Privacy & Protection

• Data Anonymization: Tools and services to ensure training data is properly anonymized to protect personal or sensitive information, adhering to data protection regulations like GDPR or HIPAA.

• Differential Privacy: We will Implement differential privacy techniques to ensure that outputs generated by AI models do not reveal sensitive information about the individuals whose data was used to train the models.

• Secure Data Handling: Ensuring secure storage and handling of the massive datasets required for training generative models. This includes encryption, access controls, and audit trails.

3. AI-Specific Threat Detection

• AI Model Behavior Monitoring: Real-time monitoring of generative AI models to detect abnormal behaviors that could indicate a compromise, such as unauthorized access, manipulation, or abnormal usage patterns.

• Anomaly Detection: Advanced analytics and AI to detect anomalies in the generative model’s outputs, which could signal a data poisoning or adversarial attack.

4. Secure AI Development Lifecycle

• Secure AI Pipeline: Services to secure the entire AI development pipeline, from data collection and model training to deployment. This could include automated security scanning of code, continuous integration/continuous deployment (CI/CD) pipelines, and ensuring security policies are enforced.

• Model Version Control and Rollback: Secure version control for AI models, ensuring that malicious modifications can be detected, and compromised models can be rolled back to a previous version.

5. Protection Against Data Poisoning

• Data Integrity Verification: Services to ensure that the data used to train generative AI models is not poisoned. This could include validation of input data, anomaly detection, and data filtering techniques.

• Defensive Training Techniques: Defensive techniques such as robust training and outlier detection to minimize the impact of poisoned data.

6. Intellectual Property Protection

• AI Model Licensing and IP Enforcement: Assisting in setting up licensing for generative AI models and enforcing intellectual property rights through technological protections like model encryption, licensing agreements, and usage monitoring.

• AI Model Reverse Engineering Prevention: Providing protection against model extraction attacks, where adversaries try to reverse engineer the AI model to steal it or gain insights into the underlying data.

7. Ethical AI and Bias Mitigation

• Bias Auditing and Mitigation: Bias detection services to ensure that generative AI models are not producing biased or unethical outputs. This involves regular auditing of the training data and the model’s output.

• Fairness and Accountability: Establish processes to monitor and ensure fairness in AI decision-making, helping organizations build transparent AI systems that can withstand ethical scrutiny.

8. Regulatory Compliance for AI

• AI Compliance Frameworks: Helping to comply with emerging AI-specific regulations and standards such as the EU AI Act or NIST AI Framework. This can include assistance in documenting AI processes, performing risk assessments, and providing AI governance structures.

• Auditability and Explainability: Services to ensure AI models are auditable and explainable, so organizations can respond to regulatory inquiries and demonstrate compliance with legal and ethical standards.

9. AI Model Governance and Policy Creation

• AI Governance Frameworks: Governance frameworks that outline how AI models are managed, who has access to them, and what security controls are in place to protect the models from external threats.

• Policy Development:  AI-specific cybersecurity policies that focus on protecting generative models, data privacy, and safe AI deployment.

10. Infrastructure and Cloud Security

• Secure AI Infrastructure: Protecting the underlying infrastructure on which AI models are built and deployed, including cloud platforms, servers, and databases. This involves ensuring that proper access controls, encryption, and monitoring are in place.

• AI Cloud Service Security: Secure cloud-based AI services, such as AWS, Azure, and Google Cloud’s AI platforms, by implementing best practices for cloud security, including identity management, encryption, and threat monitoring.

11. Incident Response for AI-Related Security Breaches

• AI Incident Response: Specialized incident response strategies tailored to AI-related threats, including data breaches, adversarial attacks, or compromised models.

• Post-Incident Recovery: Assistance for the recovery from AI-related security incidents by rolling back models, restoring data, and conducting root-cause analysis to prevent future attacks.

12. AI Usage Monitoring and Access Control

• User and Role-Based Access Control: Implementing strict access controls to ensure only authorized users can access and modify AI models. This could include multi-factor authentication (MFA), role-based access, and detailed access logs.

• AI Usage Auditing: Continuous monitoring and audit of who is using generative AI models, how they are being used, and whether usage complies with organizational security policies.

13. Phishing and Social Engineering Prevention for AI Systems

• AI-Specific Phishing Defense: Education and protection for organizations from phishing attacks targeting AI administrators or systems, potentially tricking them into giving access to AI models or data.

• AI Social Engineering Simulation: Simulated attacks aimed at testing how AI systems and their operators respond to social engineering attempts, helping organizations to shore up weak spots.

14. Continuous Security Training for AI Teams

• AI Security Awareness Training: Providing continuous education to AI developers, data scientists, and IT teams on the latest cybersecurity threats specific to generative AI models and how to mitigate them.

• Red Team/Blue Team Exercises: Simulated AI-based cyberattacks where a red team attempts to breach AI systems and a blue team defends, helping organizations prepare for real-world threats.