Large language model applications evaluate the sentence perplexity of user prompts to detect and mitigate adversarial suffixes designed to assist in the generation of sensitive or harmful content.
Information Security: Protect the LLM application against cybersecurity threats that exploit vulnerabilities in the model or its deployment environment. Implement robust security measures, including automated vulnerability detection, secure configurations, and regular updates, to mitigate risks of hacking, malware, and phishing attacks. Protect the confidentiality and integrity of sensitive components such as training data, code, and model weights, thereby preventing unauthorized access or tampering that could compromise system security.
Develop security policies aligned with regulatory frameworks and ensure governance mechanisms are robust for managing sensitive data. Assign dedicated responsibilities to enforce consistent application of security measures, including encryption and secure configurations. Introduce continuous monitoring and real-time incident management protocols to detect and respond to unauthorized access or breaches. Conduct periodic audits to validate compliance with established security guidelines and obtain certifications to manage external risks. Identify vulnerabilities across the AI data pipeline, focusing on risks from external datasets or cloud services. Secure software supply chains and dependencies, emphasizing the integrity of pre-trained models and third-party components. Use dependency mapping to eliminate gaps in data processing and storage environments. Implement access control policies to prevent unauthorized access, enable multi-factor authentication (MFA) across all endpoints, and encrypt sensitive datasets. Evaluate security protocols like firewalls and adjust configurations as needed. Regularly test models to identify biases or patterns that could expose vulnerabilities. Ensure backups and recovery mechanisms are in place, conducting regular drills to confirm resilience against outages or attacks. Apply continuous performance monitoring and prompt security patches to safeguard AI systems against evolving threats.
| ID | Operation | Description | Phase | Agent |
|---|---|---|---|---|
| SSS-02-06-03-01-01 | Implement adversarial prompt detection and monitoring | Configure language models to flag and block adversarial suffixes designed to elicit harmful or sensitive outputs. | Development | AI governance team, Security team |
| SSS-02-06-03-01-02 | Establish and enforce security protocols | Mandate MFA for accessing AI model endpoints, encrypt sensitive training datasets, and enforce strict access controls. | Preparation | Governance team, Legal team, IT operations |
| SSS-02-06-03-01-03 | Map dependencies and conduct risk audits | Audit third-party pre-trained models and datasets for embedded backdoors or biases that could compromise system integrity. | Deployment | Security team, Risk management team |
| SSS-02-06-03-01-04 | Introduce incident management and resilience protocols | Use a SOAR (Security Orchestration, Automation, and Response) platform to handle breaches and simulate incident response drills. | Post-deployment | Incident response team, IT operations, PR team |