Cyber companion, DIANNA, builds upon Deep Instinct’s prevention-first capabilities, redefining the boundaries of AI and cybersecurity

Deep Instinct, the prevention-first cybersecurity company that stops unknown malware pre-execution with a purpose-built, AI-based deep learning (DL) framework, today announced the launch of Deep Instinct’s Artificial Neural Network Assistant (DIANNA), the industry’s first AI-based cybersecurity companion that provides explainability into unknown threats. DIANNA enhances Deep Instinct's prevention-first approach to cybersecurity through its expert-grade static malware analysis, something no other solution on the market can replicate.

Powered by a large language model (LLM), DIANNA serves as a virtual AI team of malware analysts and incident response specialists. It provides deep analysis into all attacks, including never-before-seen threats, revealing the techniques employed and behaviors of files to provide a comprehensive narrative that facilitates understanding and mitigation of threats before breach.

While traditional AI tools leverage LLMs to provide human-like summarization data from existing sources like logs and reputation engines, none provide insights into how unknown attacks are malicious in nature. While valuable, their approach only offers retrospective analysis with limited context. Deep Instinct harnesses generative AI to equip DIANNA with the collective knowledge of countless cybersecurity experts, effectively embedded within the LLM, to provide in-depth malware analysis of unknown files and identify malicious intent with unparalleled accuracy.

“With the rise in AI-generated attacks, organizations can no longer be complacent or reactive in how they approach cybersecurity. It’s time to fight AI with better AI, and raise greater awareness about the unknown threats impacting businesses,” said Lane Bess, CEO of Deep Instinct. “DIANNA provides vital threat explainability, enhances our prevention-first approach, and marks a strategic shift towards a more informed, efficient, and effective cybersecurity environment.”

DIANNA seamlessly integrates with Deep Instinct's DL-powered prevention-first capabilities to provide in-depth insights into both known and unknown attack behavior through static analysis. Unlike traditional machine learning-based tools, DIANNA doesn't just provide the classification results; it provides in-depth analysis and reporting in a clear, digestible way. This transparency enables security teams to make informed decisions and prioritize threats effectively, optimizing security operation center (SOC) performance. It also reduces mean-time-to-repair (MTTR) while significantly improving job satisfaction by reducing the amount of time spent chasing false positives.

By harnessing the power of generative AI, DIANNA empowers security teams with the following capabilities:

• Unparalleled Expertise for Unknown Threats: DIANNA’s static analysis goes beyond traditional methods, offering an unprecedented level of insight into unknown scripts, documents, and raw binaries, providing valuable insights for organizations facing zero-day attacks.
• Translating Code Intent and Activity to Natural Language: DIANNA translates binary code and scripts from various languages into a natural language report. DIANNA doesn't just analyze the code; it understands the intent and the potential actions and explains what the code is designed to do, what makes it malicious, and how it might impact systems.
• Enhanced Visibility: DIANNA offers insights into the decision-making process of Deep Instinct's prevention models, allowing organizations to fine-tune their security posture for maximum effectiveness.
• Expert-Level Analysis of Threat Delivery File Types: DIANNA analyzes various file formats including binaries, scripts, documents, shortcut files, and other threat delivery file types.
• Streamlined Workflows: DIANNA automates some of the most tedious tasks of SOC analysis, freeing up security teams to focus on more strategic initiatives.

“DIANNA is the ultimate cyber companion for security teams,” said Yariv Fishman, Chief Product Officer of Deep Instinct. “There are two factors that set DIANNA apart from other AI-powered chatbots. First, its unprecedented malware analysis compresses hours of work, requiring deep cyber threat expertise, into seconds. Second, DIANNA’s ability to analyze unknown threats, including scripts, documents, and raw binary files, is unmatched. Both of these capabilities build upon our prevention-first approach and allow security teams to focus on what truly matters.”

To learn more, please visit the DIANNA webpage and register for our DIANNA webinar on Thursday, May 30 at 11 AM ET. For more information on Deep Instinct’s predictive prevention capabilities, visit www.deepinstinct.com.

About Deep Instinct

Deep Instinct takes a prevention-first approach to stopping ransomware and other malware using the world’s first and only purpose-built, deep learning cybersecurity framework. We predict and prevent known, unknown, and zero-day threats in <20 milliseconds, 750X faster than the fastest ransomware can encrypt. Deep Instinct has >99% zero-day accuracy and promises a <0.1% false positive rate. The Deep Instinct Predictive Prevention Platform is an essential addition to every security stack—providing complete, multi-layered protection against threats across hybrid environments. For more, visit www.deepinstinct.com.

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