Using Generative AI and MongoDB to Tackle Cybersecurity’s Biggest Challenges

In the ever-evolving landscape of cybersecurity, organizations face a multitude of challenges that demand innovative solutions harnessing cutting-edge technologies.

One of the most pressing issues is the increasing sophistication of cyber threats, including malware, ransomware, and phishing attacks, which are becoming more difficult to detect and mitigate. Additionally, the rapid expansion of digital infrastructures has widened the attack surface, making it harder for security teams to monitor and protect every entry and egress point. Another significant challenge is the shortage of skilled cybersecurity professionals — estimated by independent surveys to number around 4 million staff worldwide¹ — which leaves many organizations vulnerable to attack.

These challenges underscore the need for advanced technologies that can augment human efforts to secure digital assets and data.

How can generative AI help?

Generative AI (gen AI) has emerged as a powerful tool in addressing these cybersecurity challenges. By leveraging large language models (LLMs) to generate new data or patterns based on existing datasets, generative AI can provide innovative solutions in several key areas:

Enhanced threat detection and response

Generative AI can be used to create simulations of cyber threats, including sophisticated malware and phishing attacks. These simulations can help in training machine learning models to detect new and evolving threats more accurately.

Furthermore, gen AI can aid in the development of automated response systems that react to threats in real time. While this will never eliminate the need for human oversight, it will reduce the need for manual intervention and toil, allowing for quicker mitigation of attacks. For example, with the appropriate oversight it can automatically apply patches to vulnerable systems or adjust firewall rules to block attack vectors. This automated rapid response capability is particularly valuable in mitigating zero-day vulnerabilities, where the window between the discovery of a vulnerability and its exploitation by attackers can be very short.

Actionable learnings from security event postmortems

In the aftermath of a cybersecurity incident, conducting a thorough postmortem analysis is crucial for understanding what happened, why it happened, and how similar events can be prevented in the future.

Generative AI can play a pivotal role in this process by synthesizing and summarizing complex data from a multitude of sources, including logs, network traffic, and security alerts. By analyzing this data, gen AI can identify patterns and anomalies that may have contributed to the security breach, offering insights that might be overlooked by human analysts due to the sheer volume and complexity of the information.

Furthermore, it can generate comprehensive reports that highlight key findings, causative factors, and potential vulnerabilities, streamlining the postmortem process. This capability not only accelerates the recovery and learning process but also enables organizations to implement more effective remediation strategies, ultimately strengthening their cybersecurity posture.

Generating synthetic data for deep model training

The shortage of real-world data for training cybersecurity systems is a significant hurdle. Gen AI can create realistic, synthetic data sets that mirror genuine network traffic and user behavior without exposing sensitive information.

This synthetic data can be used to train detection systems, improving their accuracy and effectiveness without compromising privacy or security.

Automating phishing detection

Phishing remains one of the most common attack vectors. Gen AI can analyze patterns in phishing emails and websites, generating models that predict and detect phishing attempts with high accuracy.

By integrating these models into email systems and web browsers, organizations can automatically filter out phishing content, protecting users from potential threats.

Putting it all together: The opportunities and the risks

Generative AI holds the promise of transforming cybersecurity practices by automating complex processes, enhancing threat detection and response, and providing a deeper understanding of cyber threats. As the industry continues to integrate gen AI into cybersecurity strategies, it's crucial to remain vigilant about the ethical use of this technology and the potential for misuse.

Nevertheless, the benefits it offers in strengthening digital defenses are undeniable, making it an invaluable asset in the ongoing battle against cyber threats.

How does MongoDB help?

With MongoDB, your development teams can build and deploy robust, correct, and differentiated real-time cyber defenses faster, and at any scale.

To understand how MongoDB does this, consider that the AI technology stack comprises three layers:

1. The underlying compute (GPUs) and LLMs

2. The tooling to fine-tune models along with the tooling for in-context learning and inference against the trained models

3. The AI applications and related end-user experiences

MongoDB operates at the second layer of the stack. It enables customers to bring their own proprietary data to any LLM running on any computing infrastructure to build gen AI-powered cybersecurity applications.

MongoDB does this by addressing the hardest problems when adopting gen AI for cybersecurity. MongoDB Atlas securely unifies operational data, unstructured data, and vector data in a single, fully managed multi-cloud platform, avoiding the need to copy and sync data between different systems. MongoDB’s document-based architecture also allows development teams to easily model relationships between your application data and vector embeddings. This allows deeper and faster analytics and insights against security-related data.

MongoDB’s open architecture is integrated with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. This, combined with our industry-leading multi-cloud capabilities, allows your development teams the flexibility to move quickly and avoid lock-in to any particular cloud provider or AI technology in this rapidly evolving space.

Applying gen AI and MongoDB to real-world cybersecurity applications

Threat intelligence

ExTrac utilizes AI-powered analytics and MongoDB Atlas to predict public safety risks by analyzing data from thousands of sources. The platform initially helped Western governments foresee conflicts but is expanding to enterprises for reputational management and more.

MongoDB's document data model allows ExTrac to manage complex data efficiently, enhancing real-time threat identification. Atlas Vector Search aids in augmenting language models and managing vector embeddings for texts, images, and videos, speeding up feature development. This approach enables ExTrac to efficiently model trends, track evolving narratives, and predict risk for its customers, leveraging the flexibility and power of MongoDB to handle data of any shape and structure. Learn more in our ExTrac case study.

CyberSec assessments

VISO TRUST leverages AI to streamline the assessment of third-party cyber risks, making complex vendor security information quickly accessible for informed decision-making.

Utilizing Amazon Bedrock and MongoDB Atlas, VISO TRUST's platform automates the due diligence of vendor security, significantly reducing the workload for security teams. Its AI-powered approach involves artifact intelligence that classifies security documents, detects organizations, and predicts security control locations within artifacts. MongoDB Atlas hosts text embeddings for a dense retrieval system that enhances the accuracy of LLMs through retrieval-augmented generation (RAG), providing instant, actionable security insights. This innovative use of technology enables VISO TRUST to offer rapid, scalable cyber risk assessments, boasting significant reductions in work and time for enterprises like InstaCart and Upwork.

MongoDB's flexible document database and Atlas Vector Search play critical roles in managing and querying vast amounts of data, supporting VISO TRUST's mission to deliver comprehensive cyber risk intelligence. Learn more in our Viso Trust case study.

Steps to get started

Generative AI powered by LLMs augmented with your own operational data encoded as vector embeddings is opening up many new possibilities in cyber security. If you want to learn more about the technology and its possibilities, take a look at our Atlas Vector Search learning byte. In just 10 minutes you’ll get an overview of different use cases and how to get started.

Head over to our quick-start guide to get started with Atlas Vector Search today.

¹ Hill, M. (2023, April 10). Cybersecurity workforce shortage reaches 4 million despite significant recruitment drive. CSO.