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LinkedIn has recently unveiled a transformative initiative to enhance its security posture, particularly in vulnerability management.

LinkedIn is the social media power-hub for professionals, and with over a billion members, the platform has seen a significant increase in traffic and engagement, necessitating more efficient methods to deliver key insights to their security team. With a solution that offers top-notch insights, the LinkedIn security team will be better equipped to identify, assess, and address security weaknesses in their systems.

Overview of the Security Posture Platform (SPP)

Cybersecurity threats are constantly evolving, and traditional methods for identifying and patching vulnerabilities are proving to be inadequate against these modern challenges. Enter SSP.

LinkedInโ€™s Security Posture Platform (SPP) is a tool designed to provide a comprehensive view of the organizationโ€™s security landscape.ย 

The central component of SPP is the Security Knowledge Graph, a repository of all digital assets within LinkedIn’s infrastructure. By analyzing data across LinkedInโ€™s security systems, it enables faster threat response, predicts vulnerabilities and attack paths, and helps create proactive security strategies.

According to LinkedIn, โ€œEarly results show that SPP minimizes manual intervention, enhancing the speed of our vulnerability response by ~150%, increasing our coverage of our digital infrastructure by ~155%, through a unified platform that incorporates dynamic risk assessments and pairs automated decision-making.โ€

By integrating this solution, LinkedIn has gained detailed asset visibility, automated risk assessments, and enhanced centralized risk management, allowing for efficient prioritization, resource allocation, and improved security oversight.

Enhancing Security with Generative AI

Although the SPP was enhanced with a user-friendly interface, a flexible GraphQL playground, and an API, LinkedIn realized that those tools alone were not enough. They wanted to ensure that users like security analysts and business leaders could easily interact with the system and get quick answers to on-the-spot security questions.ย 

To achieve this, the team integrated Generative AI to remove language barriers and reduce the learning curve.

Early AI models were limited and struggled with the large data graph, so the team developed SPP AI, which uses advanced models to offer near real-time insights by simplifying complex data, making data-driven decisions easier and scalable.

SPP AI Architecture

SPP AI efficiently processes complex security data by first transforming raw inputs for Large Language Models (LLMs), then converting user queries into optimized commands. Next, it routes them to the best backend, and finally, it summarizes results for future use.

A flowchart illustrating the SPP AI Architecture. It starts with user input leading to "Inference and DataSource Selection," followed by "Context Filtering," "Multi-Query Generation," "Routing and Query Execution," and "Result Summarization," which finally produces a result. The flow interacts with an LLM (Large Language Model) and is supported by "Dynamic Prompt Generation," which interfaces with both a "Context Retrieval Interface" and a "Data Source."

SPP AI Architecture

Credit: LinkedIn

Testing and Validation

The testing process for SPP AI involves key steps. First, seed data and validation are conducted by testing initial queries and responses against standards and expert input to gauge accuracy.

Then, iterative refinement is carried out, continuously improving the system based on test results to enhance prompt generation and query handling.

Finally, human validation takes place, where experts review some of the AIโ€™s outputs to ensure accuracy and real-world applicability.

This rigorous testing framework has facilitated ongoing improvements. The LinkedIn team reports that the current GPT-4 models achieve 85%-90% accuracy, a significant increase from the 40%-50% accuracy observed during the early phases with the Davinci model from three generations ago.

Continuous Improvements and Future Considerations

LinkedIn is continuously working to make SPP AI better, constantly fine-tuning the system to improve accuracy and the quality of insights it provides. Looking ahead, the team is also considering using smaller language models for certain tasks to achieve more precise and efficient results.

Overall, LinkedIn’s Security Posture Platform marks a big step forward in managing vulnerabilities. By using AI and a detailed security knowledge graph, LinkedIn is enhancing its ability to protect its users and customers from cyber threats.

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