The idea around building this framework is to use it as a reference checklist while developing and securing a GenAI Application. To get an easier understanding of the framework we have 3 slides where we present.
- The threat landscape of GenAI applications
- The Framework itself
- An example implementation using Azure AI Stack
To give a brief explanation about the framework, there are 3 components in it.
- GenAI Application (Training Data and the Model (assets which we are securing)
- Access to the GenAI Application ( IAM & PAM )
- AI SOC ( AI augmented Security Operations Centre )
Author – Karthikeyan Krishnan, COO
The threat landscape of GenAI applications
This slide presents the growing threats to AI systems, including attacks on training data, models, and access to these training data and models. Traditional threats are now amplified by AI, leading to AI-augmented botnets that mimic human behavior, automated phishing that personalizes attacks at scale, AI-driven malware that adapts to evade detection, and enhanced social engineering using AI-generated deep fakes and personalized content.
These evolving threats require advanced defenses to keep pace with AI’s capabilities in automating and amplifying cyber attacks.
GenAI Application Framework
This framework has 4 main components of Security
- Data Security
- Model Security
- Access Security (IAM & PAM)
- AI Augmented SOC
Data Security
Three key strategies that bolster the security of training data include encryption, data loss prevention (DLP), and anonymization.
- Encryption: Encryption is a fundamental practice for protecting data, both in transit and at rest. By encrypting training datasets, organizations can ensure that even if data is intercepted or accessed without authorization, it remains unreadable. Advanced encryption standards (AES-256) are recommended for maximum security, especially when handling personally identifiable information (PII) or sensitive business data.
- Data Loss Prevention (DLP): DLP solutions monitor and control the movement of sensitive data, ensuring that critical information is not inadvertently or maliciously shared outside of the organization. For GenAI applications, DLP mechanisms can be tailored to detect and prevent the use of unauthorized training data, thereby reducing the risk of data leakage. Integration with AI model development environments helps ensure that only approved datasets are used for training.
- Anonymization: To further protect privacy, data anonymization techniques should be employed before feeding datasets into GenAI models. This process removes or masks identifiable information, allowing for the use of data in AI training without compromising user privacy. Techniques like differential privacy add noise to datasets, preventing the model from learning sensitive details while still enabling accurate training.
Incorporating these practices into GenAI development workflows not only helps secure sensitive training data but also ensures compliance with data protection regulations such as GDPR and CCPA
Model Security
Ensuring the security of Generative AI models is crucial to protecting both their functionality and the data they handle. Key aspects of model security include:
- Inference/Inversion Security: Protect models from inference attacks where attackers try to reverse-engineer data or the model itself. Techniques like differential privacy and encrypted computation can prevent sensitive information from being extracted through model outputs.
- Adversarial Robustness: Strengthen models against adversarial attacks, where subtle manipulations in input data cause incorrect outputs. Incorporating adversarial training and regular testing with adversarial examples helps improve the model’s ability to resist such attacks.
- Model Integrity: Ensure the model remains untampered and functions as intended. Cryptographic hashing and watermarking can verify the model’s authenticity, while monitoring its performance helps detect anomalies that may indicate tampering or compromise.
IAM / PAM
Your first line of defense for securing GenAI applications is implementing robust access control measures. Key components include:
- Multi-Factor Authentication (MFA): Enhances security by requiring multiple forms of verification, ensuring that only authorized users can access sensitive AI systems and data.
- Role-Based Access Control (RBAC): Assigns permissions based on user roles, limiting access to only the resources necessary for their specific tasks, reducing the risk of unauthorized access.
- Zero Trust: Operates on a “never trust, always verify” principle, continuously validating user identities and devices before granting access, regardless of their location or network.
- Just-In-Time (JIT) Access: Grants temporary, time-limited access to critical resources only when needed, minimizing the window of opportunity for potential misuse or attacks.
Together, these measures ensure comprehensive protection of your GenAI applications, mitigating risks from unauthorized access and enhancing overall security.
AI Augmented SOC
Traditional Security Operations Centers (SOC) may struggle to mitigate the risks posed by GenAI, where the scale and sophistication of attacks are significantly greater. To counter these advanced threats, SOCs must be reimagined and augmented with AI capabilities. You can’t fight a modern adversary armed with guns using bows and arrows.
This new SOC framework integrates components like User and Entity Behavior Analytics (UEBA), Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), and Security Orchestration, Automation, and Response (SOAR), all enhanced by AI.
Adapting this approach requires revising playbooks to account for AI-driven threats, reskilling security professionals, and ensuring continuous AI model training through human-AI collaboration. Additionally, audit and compliance play a critical role in ensuring the system remains secure and compliant with evolving standards.
An Example implementation of the framework with some popular tools.
Conclusion
In conclusion, by utilizing the reference framework outlined, developers and security teams can effectively identify and mitigate potential risks associated with Generative AI applications. The components presented offer a comprehensive approach to understanding threats, implementing robust security measures, and ensuring compliance. As organizations increasingly embrace AI technologies, continuous collaboration and adaptation will be crucial for maintaining a secure environment. With proactive measures and the right guardrails in place, we can confidently harness the power of Generative AI while safeguarding against emerging threats.
PS: This blog is written expanding the central ideas of the author using GenAI