Vignesh C

LLM Researcher

RAK EGA

2012.03 - Current

Vulnerability Analysis and Threat Modeling:Analyze and identify vulnerabilities in large language models, including potential adversarial attacks, data poisoning, and model inversion.
Conduct threat modeling and risk assessments to understand potential security risks specific to LLMs.
Development of Security Solutions:Design and implement robust security frameworks and protocols to protect LLMs against various types of attacks.
Develop adversarial training techniques to enhance model robustness and mitigate identified risks.
Security Audits and Penetration Testing:Perform regular security audits and penetration testing on deployed LLMs to evaluate their resilience against attacks.
Create test scenarios to assess how LLMs respond to adversarial inputs, data leakage, and other threats.
Research and Innovation:Conduct research on emerging security threats specific to LLMs and propose innovative solutions to address these threats.
Publish findings in reputable journals, participate in conferences, and contribute to the global research community on AI security.
Collaboration with Cross-functional Teams:Work closely with AI/ML engineers, data scientists, product managers, and cybersecurity teams to ensure that security considerations are integrated into the model development lifecycle.
Collaborate with legal, compliance, and ethics teams to ensure adherence to regulatory requirements and ethical guidelines.
Development of Security Best Practices:Develop and disseminate best practices, guidelines, and policies for secure development, deployment, and use of LLMs.
Educate internal teams and external stakeholders on LLM security practices, including secure data handling and model deployment.
Monitoring and Incident Response:Develop and implement monitoring tools and techniques to detect and respond to security incidents involving LLMs in real time.
Lead incident response efforts for breaches or security events related to LLMs, including conducting root cause analysis and implementing corrective actions.
Evaluation of Third-Party Components:Assess third-party tools, libraries, and APIs integrated with LLMs for potential security vulnerabilities.
Ensure that external dependencies meet security standards and do not introduce additional risks.
Regulatory and Compliance Guidance:Ensure compliance with data protection laws, AI regulations, and cybersecurity standards, including GDPR, CCPA, and other relevant frameworks.
Act as a liaison to regulatory bodies or oversight organizations, providing guidance and support on LLM security-related matters.
Mentorship and Leadership:Mentor junior researchers and engineers, providing guidance and sharing expertise on LLM security.
Lead a team of security researchers, defining research agendas, setting priorities, and overseeing security projects related to LLMs.
Tooling and Automation:Develop or integrate security tools to automate vulnerability detection, model validation, and testing processes.
Build and maintain infrastructure for continuous monitoring and improvement of LLM security.
Adversarial Red Teaming:Simulate sophisticated adversarial attacks and evaluate the effectiveness of defensive mechanisms.
Coordinate red team exercises focused on understanding how LLMs could be exploited in real-world scenarios.
Managed multiple research projects simultaneously, ensuring timely completion within budget constraints.
Policy Advocacy and Ethical Oversight:Advocate for policies and best practices in AI governance, especially around responsible use and deployment of LLMs.
Contribute to discussions on ethical AI, privacy, and fairness, ensuring LLMs do not propagate biases or harmful content.

Summary

Overview

Work History

LLM Researcher

Education

Master of Science - Computer Applications

Skills

Timeline

LLM Researcher

Master of Science - Computer Applications

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