What Is Generative AI (GenAI)?

Gartner’s Top Cybersecurity Trends of 2025 report emphasizes the growing influence of generative AI (GenAI), highlighting new opportunities for organizations to enhance their security strategies and implement more adaptive, scalable defense models. While 2024 was expected to focus on developing minimum viable products, by 2025, we are seeing the first meaningful integration of generative AI into security workflows, delivering substantial value. And by 2026, according to Gartner, the emergence of new approaches, such as “action transformers,” combined with more mature GenAI techniques, will drive semiautonomous platforms that will significantly augment tasks executed by cybersecurity teams.

​​What Is Generative AI?

Generative AI refers to machine learning (ML) models that create new content by learning patterns from existing data. As Gartner explains, GenAI “learns from existing artifacts to generate new, realistic artifacts at scale.” In simpler terms, you train these models on massive datasets (text, code, images, etc.), and then generative AI can produce similar content on demand. 

Technically, most modern GenAI tools are built on foundation models—huge neural networks trained on broad datasets. These models require enormous compute power, but once trained, they can be fine-tuned for many tasks (from writing detection code to drafting threat reports). GenAI creates rather than just analyzes, and it is evolving extremely rapidly. Every few months brings new, more capable models. However, it’s important to note that GenAI outputs still need human oversight as AI-generated results can be inaccurate or biased, making human validation essential.

How Generative AI Works? 

Generative AI is built on the foundation of machine learning, particularly a subfield called deep learning, which uses layered networks of algorithms known as neural networks. These networks are inspired by how neurons operate in the human brain, enabling systems to learn from large datasets and generate results autonomously.

One of the most influential architectures in generative AI is the transformer model. Transformers process data in parallel using mechanisms that help the model understand context over long sequences. This makes them especially effective for natural language tasks like summarization, translation, and code generation. Large language models (LLMs), often used in GenAI applications, are typically based on transformer architecture and trained on massive datasets that include code repositories, threat intelligence reports, system logs, and more. Using a method called unsupervised learning, the model learns to predict the next word or element in a sequence. Over time, it builds an internal representation of grammar, logic, style, and meaning.

Here’s how it works:

1. Training Phase: The model processes huge amounts of unstructured text or code. It doesn’t memorize content but learns relationships between words, phrases, syntax, and concepts.
   
   
2. Fine-Tuning: The base model is then refined using domain-specific data, like cybersecurity logs, threat reports, or detection rules, to align its outputs with specific needs.
   
   
3. Prompting: When a user provides an input (a prompt), the model generates a response by predicting the most likely continuation, word by word, or token by token.
   
   
4. Context Awareness: Modern GenAI tools can consider a large amount of context at once, allowing them to understand complex queries or multi-step tasks, making them especially useful in cybersecurity workflows.

For example, in a security operations context, you might input a threat summary or log snippet, and GenAI could generate a detection rule, summarize the threat, or suggest next steps—all based on its understanding of similar data it has seen before.

The power of GenAI lies in its ability to synthesize, translate, and generate insights quickly, turning raw, complex data into actionable information. However, outputs are probabilistic, not deterministic, meaning human review remains critical for high-assurance tasks.

GenAI In Cybersecurity

Generative AI (GenAI) is increasingly becoming an essential part of modern cybersecurity operations. Rather than replacing human expertise, GenAI augments it, acting as a digital co-pilot that accelerates analysis, automates repetitive tasks, and helps defenders stay one step ahead of evolving threats.

Security teams are already piloting GenAI across a range of workflows, from threat intelligence to vulnerability management. For example, GenAI tools can summarize CVE advisories or generate detection rules based on observed behavior patterns. In alert triage, GenAI helps reduce noise by identifying likely false positives or clustering related incidents. And when it comes to documentation or training, AI can draft policy updates or simulate phishing emails for awareness campaigns.

These capabilities are steadily becoming embedded into existing platforms — not as standalone tools, but as integrated enhancements to enrich data, accelerate decision-making, and streamline defense. While human oversight remains essential, GenAI offers meaningful productivity gains, allowing security professionals to focus on higher-impact work.

What Are Pros and Cons of Generative AI in Cybersecurity?

GenAI is being used today to augment cybersecurity tasks – from “grunt work” like summarizing data and triaging alerts to more creative tasks like suggesting detection rules or training content. Every use case still involves human experts in the loop, but the AI handles repetitive analysis and documentation, helping teams do more with less. Here are some examples of how GenAI can support defenders:

• Threat Intelligence & Incident Response: GenAI can digest and summarize complex threat data. For instance, it can summarize lengthy threat reports or CVE advisories, highlighting the most critical information, indicators of compromise, or attackers’ TTPs. By automatically extracting root causes and key details from mountains of data, GenAI accelerates investigations and helps analysts stay ahead of threats.
  
  
• Detection Engineering: Security engineers are exploring GenAI to help write detection rules or queries. For example, by providing an AI model with examples of malicious activity and how they were detected, the model can draft new detection logic. Alternatively, GenAI can help with detection logic summarization, CTI enrichment, detection code translation across various SIEM, EDR, or Data Lake languages, and more. All of the mentioned features are currently supported by SOC Prime’s Uncoder AI. 
  
  
• False-Positive Reduction: GenAI can help minimize false positives by acting as a secondary analysis layer. It can evaluate alerts alongside associated code and provide reasoning in natural language, helping teams quickly distinguish between benign and suspicious events. According to industry forecasts, by 2027, GenAI is expected to reduce false positive rates in application security testing and threat detection by 30%, thanks to its ability to refine and contextualize outputs from traditional analysis techniques.
  
  
• Vulnerability Management: GenAI can help prioritize and even help to fix vulnerabilities. An AI model can scan code or configurations for security weaknesses and point out risky patterns. It can then rank those flaws by potential impact and suggest remediation steps. GenAI tools act like smart code reviewers, helping development and security teams address the most critical vulnerabilities first.
  
  
• Analysis & Summarization: Beyond structured data, GenAI can summarize unstructured or semi-structured information. It can take alert messages, Slack chats, or system logs and convert them into plain-language summaries. For instance, if thousands of log entries spike, a GenAI model could describe the anomaly trend in a paragraph. This frees analysts from wading through every detail; instead, they get quick insights generated by AI.
  
  
• Training & Policy: Some teams use GenAI to generate realistic training scenarios. For example, it can craft personalized phishing email examples for staff or write a tailored explanation of a new security policy. GenAI can analyze existing policies to spot gaps or even draft revised policies based on best practices. This makes maintaining awareness and documentation more efficient, although all outputs are validated by humans.

While generative AI presents significant opportunities for enhancing cybersecurity operations, it also introduces new challenges. Adversaries are adopting the same AI-driven tools to increase the speed, scale, and sophistication of their attacks. In fact, Gartner expects attackers to gain the same benefits that most industries are expecting from GenAI: productivity and skill augmentation. GenAI tools will enable attackers to improve the quantity and quality of attacks at a low cost. They will then leverage the technology to automate more of their workflows in areas such as scan-and-exploit activities. The offensive side doesn’t require any compliance and legal regulations to be implemented in attack strategies. This delay can extend to hours or even days for complex threats, giving attackers a substantial advantage. 

Emerging AI regulations are also adding a new layer of complexity, introducing legal risks and tactical limitations that are not yet fully understood. Gartner projects that, through 2025, generative AI will lead to a 15% increase in cybersecurity resources needed to secure it, resulting in higher spending on application and data security. The lack of transparency surrounding generative AI mechanisms and data sources is causing security leaders to hesitate in automating actions based on the outputs of generative cybersecurity AI applications. As a result, mandatory approval workflows and detailed documentation will be necessary until organizations gain sufficient trust in these systems to incrementally increase automation levels. 

SOC Prime’s AI SOC Ecosystem

Leading the way in Detection as Code, AI-powered detection engineering, and automated threat hunting, SOC Prime brings together cutting-edge security solutions into a powerful, vendor-agnostic AI SOC ecosystem. It combines SOC Prime technology, driven by AI and collective expertise, with our partners’ innovations to deliver unparalleled cyber defense capabilities.

SOC Prime’s AI SOC Ecosystem has community-driven expertise at its core, reflecting the major trend of current AI adoption aimed mainly at augmenting routine tasks and acting as a co-pilot for security teams. This resonates with the game-changing threat-informed defense approach, which encourages a culture of continuous improvement in cybersecurity backed by the combined expertise of Blue, Red, and Purple Teams to test and improve their defense capabilities continuously in a collaborative manner to drive up effectiveness. Built on a five-year strategic cycle, this approach leverages open standards to ensure transparency across multiple tools and diverse techniques, allowing organizations to integrate and adapt their defenses as threats evolve. By drawing on threat intelligence that reflects real-world, military-grade tactics and techniques, the threat-informed defense approach empowers cybersecurity teams to anticipate, detect, and respond to the actions of highly sophisticated attackers.

Aligning with Gartner’s prediction that AI deployments enhancing human expertise will outperform single-purpose analytics, SOC Prime’s AI ecosystem is designed to amplify the capabilities of cybersecurity teams by combining cutting-edge machine learning with community-driven knowledge. At the heart of this ecosystem is the SOC Prime Platform, serving three core products. Threat Detection Marketplace, which acts as the world’s largest Detection-as-Code library, offering curated detection content and actionable threat intelligence. Uncoder serving a private IDE and AI co-pilot for threat-informed detection engineering. And Attack Detective, which is an enterprise-ready SaaS for advanced threat detection and automated threat hunting.

To power these innovations, SOC Prime leverages a variety of privately hosted market-leading large language models (LLMs), like Llama. We also maintain a suite of purpose-built AI/ML models:

• SOC Prime Retrieval-Augmented Generation (RAG) LLM model. Powered by RAG database with SOC Prime’s unique collection of over 500,000 rules and queries mapped to 11,000 high-quality metadata labels, this LLM model enables context-enriched detection rule generation from raw CTI data.
  
  
• SOC Prime MITRE ATT&CK® Tagging ML model. Building on our innovation of tagging Sigma rules with ATT&CK, introduced at the first EU ATT&CK Community Workshop in 2018, SOC Prime curates the ML model that enables automated ATT&CK (sub)technique tagging for detection code in Sigma and Roota. Trained on the world’s largest dataset of over 50,000 rules & queries, including native SIEM, EDR, and Data Lake queries, Sigma rules, and top-quality translations.
  
  
• SOC Prime language detection ML model. SOC Prime curates this ML model to automate query language identification across 44 different SIEM, EDR, and Data Lake formats, trained on SOC Prime’s dataset of over 500,000 rules & queries, including native rules, Sigma rules, and top-quality translations.

Let’s see how GenAI can power up daily cybersecurity operations using the example of Uncoder, which recently received a major update and offers a variety of AI-powered features for threat-informed detection engineering, 100% free. 

Uncoder is a private non-agentic AI for Detection Engineers and SOC Analysts. The latest updates for Uncoder AI, released in May 2025, introduce a robust set of features designed to enhance how detection rules are created, translated, and optimized across the most popular technologies, acting as a game-changer for security teams to stay ahead in the evolving cybersecurity landscape. 

Uncoder AI is powered by a combination of SOC Prime’s proprietary machine learning models—trained on the world’s largest dataset of over 500,000 detection rules and queries, enriched with 11,000+ contextual labels—and select integrations with market-leading public LLMs. For the majority of AI-powered features, Uncoder AI uses Llama 3.3 customized for detection engineering and threat intelligence processing. This model operates entirely within SOC Prime’s SOC 2 Type II-compliant private cloud, ensuring full control over data, strict privacy, and IP protection. Support for additional LLMs is planned, offering users more flexibility while maintaining a privacy-first approach.

The following AI-powered capabilities backed by Uncoder AI are now available for free:

• • Rule/query generation from Threat Report. Analyzes the provided threat report and generates a rule/query to detect the described behavior. 
    
    
  • Rule/query generation via Custom Prompt. Analyzes the provided custom prompt and generates a rule/query based on the user’s instructions.
    
    
  • Decision tree summarization. Analyzes a query/rule and explains how it works step by step, with all the embeddings, branches, and other intricate logic.
    
    
  • Short and full rule/query summarization. Analyzes a rule/query and provides security engineers with a detailed yet clear explanation of the detection logic and all the fine points involved.
    
    
  • Query optimization. Analyzes a query and either confirms it’s optimal or suggests performance improvements.
    
    
  • Rule syntax & structural validation. Analyzes the syntax and structure of a rule/query and flags errors, suggests improvements, or confirms that everything is correct.
    
    
  • Attack Flow generation (Beta). Analyzes the provided threat report or other description of malicious activities and visualizes it in the form of an Attack Flow. 
    
    
  • MITRE ATT&CK tag prediction. Uses SOC Prime’s privately hosted ML model to map a Sigma rule to ATT&CK techniques and subtechniques.
    
    
  • AI-assisted cross-platform translation. Translates across platform-native languages. Basic query logic is translated natively, while advanced function translation is generated by a third-party AI OpenAI’s GPT-4o mini model.
    
    
  • Supercharging into Roota. Turns a platform-specific rule or query into a Roota rule and enriches it with metadata using SOC Prime’s proprietary algorithms and AI.

Register for SOC Prime Platform to start exploring the AI-powered features and experience how GenAI acts as a game-changer to boost the efficiency of SOC operations.