Apr 10, 2025
Agentic AI & Cybersecurity: A Powerful Partnership
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AI is used in cybersecurity to automate and enhance security operations, bridging the gap between threat detection and incident resolution. By automating routine tasks and orchestrating complex workflows, AI helps understaffed security teams work more efficiently. This allows analysts to focus on more critical tasks while AI handles functions like streamlined report writing, improved case management, and enhanced phishing detection.
What is AI in Cybersecurity?
AI in cybersecurity is the application of artificial intelligence and machine learning to automate security operations. This technology helps security teams respond to threats more efficiently by automating tasks and orchestrating actions across various security tools.
Ultimately, the goal is to bridge the gap between threat detection and incident resolution, allowing human analysts to focus on complex decision-making and strategic tasks while AI handles routine and time-consuming work.
Benefits of AI in Cybersecurity
Streamlining Incident Response
AI in cybersecurity offers significant benefits by transforming security operations from a manual, reactive process into an automated, proactive defense. At its core, AI addresses the most critical and often inefficient phase of security: the “last mile” of response. By automating the complex workflows and orchestrating actions across disparate security tools, AI helps close the gap between initial threat detection and final resolution, ensuring security teams can respond to incidents with speed, consistency, and at scale.
Empowering Security Operations Teams
A key advantage of AI-driven automation is its ability to empower SecOps teams. It resolves incidents faster and with greater accuracy by handling the routine, time-consuming operational tasks that traditionally bog down analysts. This allows human experts to focus on complex, high-stakes decisions and strategic threat hunting, ensuring that AI complements, rather than replaces, human oversight where it is most needed.
7 AI Use Cases in Cybersecurity
Swimlane Turbine is an AI-enhanced security automation platform that supports a wide range of use cases designed to optimize security operations and empower analysts. From automating routine tasks to streamlining incident response and report writing, Turbine helps alleviate the burden on understaffed SOC teams while improving efficiency and accuracy. One of the components of Turbine is Hero AI, which integrates human and machine intelligence to optimize SecOps workflows and maximize return on investment.
Below are seven key Hero AI use cases that demonstrate how Hero AI and automation enhances cybersecurity operations across multiple levels.
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 where experienced personnel are in high demand. AI enables faster incident response, investigation, and resolution, which is vital in situations where every second counts.
2. Enhances Your Security Automation:
AI enhances automation systems, adding an intelligent layer that can streamline tasks previously handled manually. This enhanced automation not only improves efficiency but also reduces the workload on security teams. For a practical example of how AI-enhanced automation can support security operations, read how AI scripting simplifies automation.
3. Supports Understaffed SOC Teams:
With many cybersecurity teams facing understaffing, AI and automation helps alleviate this burden by acting as an additional resource. AI-enhanced automation can handle tasks that would typically require multiple employees, particularly in lower-level (Tier 1) operations. It accelerates the onboarding process for new analysts by reducing the learning curve and 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 is incredibly effective at generating reports, summaries, and regulatory documentation that many analysts dread. By automating this process, AI saves time and ensures consistency and compliance with regulatory requirements.
- Reporting in any language for stakeholders or clients whose native language may be different.
- Help streamline CISO reporting for the board and executives to put highly technical information into a communication that business executives understand.
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, as well as 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. By integrating contextual knowledge, Hero AI empowers security teams to respond more efficiently to threats and enhances the overall effectiveness of security operations.
6. Improves Shift Transitions:
AI is also valuable during shift changes between Security Operations Center (SOC) analysts, particularly 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. This helps analysts address one of the most pervasive cybersecurity threats with greater accuracy and efficiency.
What is the Future of AI in Cyber Security?
The future of AI in cybersecurity lies beyond simple task automation. As the threat landscape grows more complex, AI is poised to evolve into a strategic, “agentic” force that works alongside human analysts to solve multifaceted problems. This new era of AI will be defined by private, domain-specific models that are trained on an organization’s unique data, allowing them to provide highly accurate and contextual insights. The goal is to move from reactive defense to proactive, predictive security operations, with AI acting as a trusted co-pilot that can orchestrate complex responses and empower teams to make better decisions faster, ultimately transforming security from a reactive burden into an intelligent, data-driven defense.
AI in Cybersecurity Examples
AI Use Cases in Banking and Financial Services
Fraud Detection & Prevention: AI can monitor transactions in real-time, analyzing patterns and anomalies to detect fraudulent activity common to the banking and financial services sector. Machine learning algorithms continuously adapt to new tactics used by cybercriminals, enabling financial institutions to stay ahead of emerging threats while maintaining operational efficiency.
AI Use Cases in Healthcare
Data Protection: AI helps healthcare organizations secure sensitive patient data by identifying vulnerabilities and potential breaches. Automated processes ensure compliance with regulations like HIPAA, while enhancing the organization’s ability to respond swiftly to cyberattacks targeting critical healthcare data.
AI Use Cases in Government
Nation-State Threats: Government agencies leverage AI to detect and respond to cyber espionage, nation-state attacks, and other advanced persistent threats (APTs). AI-enhanced automation rapidly analyzes large volumes of data, providing security teams with actionable insights for the timely mitigation of sophisticated attacks.
AI Use Cases in Retail
Customer Data Security: AI plays a key role in protecting customer information from compromise, especially as retailers manage vast amounts of personal and payment data. Automated threat detection ensures compliance with industry standards like PCI DSS while identifying and addressing potential security risks in real-time.
AI Use Cases in the Energy Sector
Critical Infrastructure Protection: AI is essential in safeguarding critical infrastructure in the energy sector. With increasing reliance on IoT and connected systems, AI-enhanced automation monitors and defends against potential breaches, ensuring operational continuity and robust security against cyber threats.
AI Use Cases in Education
Network Security: Schools and universities are frequent targets of cyberattacks. AI enhances institutions’ ability to protect by automating the monitoring of unauthorized access, detecting malicious activity, and securing both administrative and student data. This allows educational institutions to better defend against evolving cyber threats.
How to Implement AI Use Cases with Swimlane Turbine
Swimlane Turbine combines generative AI, low-code capabilities, and advanced automation to tackle the most challenging problems across your entire security organization. Turbine addresses key concerns around data privacy and security, offering a secure, reliable way to implement AI use cases in sensitive sectors like cybersecurity. With Turbine’s Hero AI, organizations can utilize key features like a private large language model (LLM), AI case summarization, recommended actions, augmented reporting, crafted AI prompts, and schema interface. Hero AI’s LLM operates in a secure, isolated environment within the Turbine cloud, ensuring that no customer data is shared externally, giving organizations full control over their data.
By focusing on security and privacy, Swimlane Turbine empowers organizations to leverage AI for automation, threat detection, and reporting—without compromising data integrity. AI within Swimlane is designed to enhance security operations while maintaining the highest standards of data protection.
Use of AI in Cyber Security FAQs
How to Identify AI Use Cases?
Identifying AI use cases in cybersecurity largely depends on the specific needs of each industry. Every sector faces unique challenges, and AI’s adaptability allows it to be tailored to address those challenges. Below are key areas where AI, built on the foundation of automation, is making a significant difference across industries.
What is the role of AI and machine learning in cybersecurity?
The overall role of AI is to enhance and automate cybersecurity operations, enabling organizations to detect and respond to threats faster and more effectively than traditional methods. Machine learning is a key component of AI that allows systems to analyze vast amounts of data, identify patterns, and learn from experience without being explicitly programmed. This allows security tools to continuously adapt to new and evolving threats.
How is AI automation used in cybersecurity?
AI automation streamlines time-consuming tasks to improve efficiency and reduce the burden on security teams. This includes automatically triaging alerts to reduce false positives, orchestrating incident response actions, and generating reports. By handling these routine processes, AI frees up human analysts to focus on complex threat investigations and strategic security initiatives.
TL;DR: AI in Cybersecurity
AI in cybersecurity goes beyond threat detection to automate the “last mile” of security, bridging the gap between detection and response. By handling routine tasks and orchestrating workflows, AI empowers security teams to work faster and more accurately. The future of AI in this space is an agentic AI that works as a partner to human analysts, transforming security into a more proactive and strategic discipline.
TAG Cyber Tech Report: Using AI for SecOps Automation
The analyst report begins with a brief overview of the SOAR market, and the story of how Swimlane transformed from a SOAR to AI-enhanced security automation platform. To further understand Swimlane’s use of AI, read the full report.
