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Highly focused Cybersecurity professional , subject matter expertise in all aspects of…

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  • Google

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Publications

  • Automatically Detecting Expensive Prompts and Configuring Firewall Rules to Mitigate Denial of Service Attacks on Large Language Models

    Technical Disclosure Commons

    Denial of service attacks on generative artificial intelligence systems, e.g., large language models (LLMs), can include sending LLMs requests that include expensive prompts designed to consume computing resources and degrade model performance. This disclosure describes techniques to automatically detect such prompts and then configure firewall rules that prevent such prompts in subsequent requests from reaching the LLM. Per the techniques, prompts provided to an LLM are matched against input…

    Denial of service attacks on generative artificial intelligence systems, e.g., large language models (LLMs), can include sending LLMs requests that include expensive prompts designed to consume computing resources and degrade model performance. This disclosure describes techniques to automatically detect such prompts and then configure firewall rules that prevent such prompts in subsequent requests from reaching the LLM. Per the techniques, prompts provided to an LLM are matched against input and output token size as well as resource utilization to identify prompts that deviate significantly from a baseline. Expensive prompts are identified, and semantically similar prompts are automatically generated using the same LLM or another model. A subset of the generated prompts that are semantics similar to expensive prompts are identified by comparing respective vector embeddings. The subset of prompts and the received expensive prompts are provided to a pre-trained LLM that generates firewall rules, e.g., web application firewall (WAF) rules. Incoming requests from applications are evaluated based on the rules, and expensive prompts are blocked from reaching the LLM or are rate-limited.

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  • Method to isolate tenancies for Large Language Modules (LLMs) Applications

    Technical Disclosure Commons

    Large language models (LLMs) and other types of generative artificial intelligence can be used in a wide variety of business applications. However, there is a possibility of data leakage from LLM responses when an LLM is used in shared multi-tenant environments where each tenant has respective private datasets. Deploying individual adapter layers for each tenant can provide data isolation. However, such implementations can be complex and costly. This disclosure describes techniques to create…

    Large language models (LLMs) and other types of generative artificial intelligence can be used in a wide variety of business applications. However, there is a possibility of data leakage from LLM responses when an LLM is used in shared multi-tenant environments where each tenant has respective private datasets. Deploying individual adapter layers for each tenant can provide data isolation. However, such implementations can be complex and costly. This disclosure describes techniques to create and maintain a single model that can serve multiple tenants, with security controls for multi-tenancy services to isolate customer data efficiently. Data for different tenants is signed with their respective tenant-specific keys and is then appended with the tenant-specific signature prior to training/tuning a model or use by the model at inference time. When a business application of a particular tenant requests a response from the LLM, the response is generated using the adapter layer. The response includes data citations that are verified prior to the response being provided to the business application. The verification is based on the tenant-specific signature in the citation to ensure that only data that belongs to the particular tenant that requested the response is included.

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  • Official Google Cloud Professional Security Certification guide

    Packt

    Google Cloud security offers powerful controls to assist organizations in establishing secure and compliant cloud environments. With this book, you'll gain in-depth knowledge of the Professional Cloud Security Engineer certification exam objectives, including Google Cloud security best practices, identity and access management (IAM), network security, data security, and security operations.

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  • A System and Method to Optimize LLM's Prompt Security Evaluation

    Technical Disclosure Commons

Courses

  • Advanced Computer Vision with Deep Learning

    ADSP 32023

  • Advanced Machine Learning & Artificial Intelligence (Tranformers)

    ADSP 32017

  • Big Data Platforms

    ADSP 31013

  • Data Mining Principles

    ADSP 31008

  • Linear Algebra

    ADSP 37016

  • Linear and Non-Linear Models

    ADSP 31010

  • Machine Learning & Predictive Analytics

    ADSP 31009

  • R for Data Science

    ADSP 37020

  • Statistical Analysis

    ADSP 31007

Languages

  • Hindi

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  • Marathi

    -

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