Generative AI is reshaping cybersecurity by providing advanced threat detection, automation, and data analysis capabilities. However, as generative AI evolves, its potential applications will become limitless. Its ability to generate insights from complex data enables faster, more accurate identification of threats, helping organizations enhance their security posture and stay ahead of cyber attacks. 

Read on to explore how generative AI is applied in cybersecurity and its potential benefits.

How Has Generative AI Affected Security?

Generative AI has transformed cybersecurity by enhancing threat detection and incident response by analyzing vast amounts of data in real-time. Leveraging machine learning and deep learning techniques allows organizations to identify and mitigate emerging threats faster and more accurately than traditional methods.

Generative AI in Cybersecurity Benefits

While generative AI has caused positive and negative disruptions, it ultimately enhances cybersecurity operations by streamlining workflows, accelerating response times, and minimizing human error. Its ability to continuously learn from data and adapt to new threats makes it a valuable tool for proactive defense, allowing security teams to stay ahead of evolving cyberattacks.

Swimlane Hero AI Benefits 

Swimlane Turbine’s Hero AI combines the power of generative AI and automation to transform security operations. Enhancing threat detection, automating workflows, and providing intelligent insights empowers teams to work faster and smarter, ultimately improving efficiency and reducing risk. Below, we highlight Hero AI’s key benefits to your security operations.

1. Proprietary and Private LLM

Your security data is too critical to be exposed to a public LLM. That’s why we’ve built our own proprietary and private LLM to be the foundation for all Turbine native Hero AI features that involve security data.

2. Crafted AI Prompts

Take control of your AI experience in Turbine with Hero AI Crafted Prompts. Leverage data in alerts, cases, intelligence, or automation pipelines to prompt the LLM of your choice, including Swimlane’s own, to apply AI throughout any workflow.

3. AI Case Summarization

Understand even the most intricate cases, alerts, and intelligence faster and easier than ever before. Hero AI Case Summarization will help you categorize, prioritize, remediate, and mitigate issues intuitively and easily.

4. Schema Inference

Easily and instantly map dynamic data across any use case. Hero AI Schema Inference is an intelligent testing and curation tool that will provide complete control with minimal effort. It will empower you to build robust and efficient playbooks with ease.

Examples of AI in Cybersecurity 

AI-driven tools such as AI automation platforms and intrusion detection systems (IDS) use generative AI to predict and prevent security breaches. There are numerous AI use cases today that are helping solve cybersecurity challenges. AI cybersecurity use cases range from automating cybersecurity processes to enhancing decision-making in complex security environments. AI’s ability to adapt and learn makes it a critical tool for security teams, helping them operate more effectively and address the growing landscape of cyber threats. Some examples of AI in cybersecurity include:

1. Helps Analysts Do More, Faster:

AI can automate routine tasks for security analysts, such as processing large datasets and generating actionable insights. This allows analysts to focus on higher-level strategic tasks, which is crucial for understaffed security teams with experienced personnel in high demand. 

2. Enhances Your Security Automation: 

AI enhances automation systems, adding an intelligent layer to streamline tasks previously handled manually. This enhanced automation improves efficiency and reduces the workload on security teams. 

3. Supports Understaffed SOC Teams: 

Many cybersecurity teams face understaffing, and AI and automation help alleviate this burden by acting as additional resources. AI automation can handle tasks requiring multiple employees, particularly in lower-level (Tier 1) operations. It accelerates the onboarding process for new analysts by reducing the learning curve. It empowers Tier 1 analysts with insights and capabilities, enabling them to operate with the efficiency and skillset of higher-tier (T3) analysts.

4. Streamline Report Writing: 

AI effectively generates reports, summaries, and regulatory documentation that many analysts dread. By automating this process, AI saves time and ensures consistency and compliance with regulatory requirements. 

5. Assist in Case Management: 

AI can assist the capabilities of less experienced analysts by suggesting next steps based on similar cases and historical data. It can answer questions like “Where have we seen this issue before?” and provide tailored recommendations, allowing Tier 1 analysts to operate at the level of Tier 3 analysts.

Hero AI’s Context-Aware Recommended Actions feature elevates this process by leveraging established cybersecurity frameworks, such as NIST CSF and MITRE ATT&CK, and insights from customers’ best practices. This advanced case summarization offers concise, relevant actions to resolve incidents effectively, ensuring that decisions are grounded in proven methodologies. 

6. Improves Shift Transitions: 

AI is also valuable during shift changes between Security Operations Center (SOC) analysts, mainly when 24-hour shifts are involved. Instead of asking overworked analysts to write status reports at the end of their shifts, AI can generate comprehensive updates, enabling seamless transitions between shifts.

7. Phishing Detection and Analysis: 

Phishing detection is a common issue faced by every organization. AI can inspect suspicious emails, analyze patterns, and explain why an email might be a phishing attempt. The use of AI for phishing detection and analysis helps analysts address one of the most pervasive cybersecurity threats more accurately and efficiently.

Tip: If you’re intrigued by this, jump to our blog on how AI is used in cybersecurity. 7 AI Use Cases to dive deeper into AI automation use cases. 

Generative AI Security Risks 

While generative AI holds vast potential for improving security, it also presents new challenges. One significant risk is the threat of adversarial attacks, where malicious actors manipulate AI models to evade detection or disrupt security systems. This type of malicious use of AI can undermine the reliability of AI-driven security solutions and make organizations vulnerable to sophisticated cyberattacks. 

Additionally, improperly implemented or undertrained AI models can generate false positives, leading to unnecessary alerts or, worse, miss critical threats, leaving organizations exposed. As generative AI continues to evolve, organizations must ensure their AI solution is regularly updated and refine their AI models to maintain their effectiveness and safeguard against emerging threats.

Top 3 Security Risks for Generative AI:

1. Adversarial Attacks – Cybercriminals can manipulate AI models to bypass security measures, making it difficult for systems to detect and respond to threats accurately.
2. False Positives and Missed Threats – Poorly trained or improperly configured AI systems can generate excessive alerts or fail to identify critical vulnerabilities, compromising security effectiveness.
3. Data Poisoning – Attackers can inject malicious data into the AI training models, causing the system to learn incorrect patterns or make flawed decisions that jeopardize security operations.

Because of these security risks, organizations must conduct thorough research and select AI solutions that implement and use AI securely, ensuring they prioritize safety and reliability. Turbine’s Hero AI, powered by a private LLM, offers a secure, tailored solution that protects organizations by delivering advanced AI automation and decision-making without compromising data privacy or security. 

AI Tools for Cybersecurity 

AI tools like Swimlane Turbine’s Hero AI leverage generative AI to automate security workflows, accelerate threat detection, and enhance decision-making. These tools integrate machine learning and automation to reduce SOC analyst workloads and improve overall security posture.

In a recent interview, Tracy Webb, Director of Information and Cybersecurity Operations at Global Data Systems (GDS), shared how Turbine revolutionized his SecOps team’s ability to manage a significantly larger number of cases effectively. 

As early adopters of Swimlane’s Hero AI, Webb explained that Hero AI has given his analysts the game-changing ability to ask for exactly what they need and get an easily digestible format back. Since transitioning to Turbine, the team has completed an impressive 5,000 cases in a remarkably short period. Webb shared, “What I do today with seven engineers and analysts would probably take an additional 20 staff members without Swimlane Turbine.”

More on Generative AI for Cybersecurity 

Generative AI’s potential in cybersecurity is still expanding, with new use cases emerging for predictive analysis, automated vulnerability management, and incident response. As generative AI continues to evolve in 2025, it will play a pivotal role in shaping the future of cybersecurity by enabling faster, smarter, and more efficient threat management.

What is generative cybersecurity AI?

Generative cybersecurity AI refers to machine learning models that create or predict data to detect and mitigate cybersecurity threats. Analyzing historical data and generating potential attack scenarios helps security teams stay ahead of emerging threats.

What generative AI means for cybersecurity in 2025?

In 2025, generative AI will be integral to cybersecurity, enabling organizations to defend against sophisticated and evolving threats proactively. It will enhance real-time threat detection, automate response actions, and help security teams manage larger volumes of data, significantly improving efficiency and reducing human error.

How does cybersecurity AI work?

Cybersecurity AI uses algorithms and machine learning models to analyze large datasets for normal and abnormal behavior patterns. It helps detect anomalies, predict potential security breaches, and automate responses, allowing faster decision-making and improved defense strategies.

Can generative AI replace cybersecurity jobs? 

Generative AI is unlikely to replace cybersecurity jobs entirely but will enhance human capabilities. AI can automate repetitive tasks and improve decision-making, allowing cybersecurity professionals to focus on higher-level strategic initiatives and complex problem-solving rather than the day-to-day tedious and repetitive tasks. 

Discover our blog “Will AI Take Over CyberSecurity Jobs” for more information.