The modern digital landscape is defined by ever-expanding attack surfaces, from cloud infrastructure and IoT devices to complex software supply chains. For security teams, the volume, velocity, and sophistication of cyber threats have outpaced human capacity. Security analysts are inundated with alerts, making it nearly impossible to distinguish real threats from false positives. This is where artificial intelligence (AI) is becoming an indispensable ally, shifting security operations from a reactive posture to a proactive and predictive one.
While AI has been a part of the cybersecurity toolkit for years, its role is rapidly evolving. It’s no longer just about pattern recognition; it’s about augmenting human expertise, automating complex responses, and creating a more resilient security posture.
Key Takeaways:
- AI cybersecurity detects issues earlier, correlates signals, and slashes MTTR and false positives.
- Agentic AI delivers autonomous remediation—blocking, isolating, and patching attacks in real time.
- Generative AI accelerates incident reporting, secures code, and powers targeted phishing training.
- Integrated SIEM/SOAR/EDR workflows reduce noise, improve explainability, and scale response.
- Operational resilience keeps AI models, data pipelines, and response automations continuously available.
What is AI in cybersecurity?
AI in cybersecurity is the application of machine learning and advanced algorithms to detect, predict, and respond to cyber threats automatically and at scale. Its purpose is not to replace security analysts, but to equip them with intelligent tools that can analyze massive datasets in real time, identifying malicious activities that would otherwise go unnoticed.
Agentic AI takes this a step further, empowering AI agents to not only identify threats but also to execute complex, multi-step response actions without direct human intervention. This moves security from detection to autonomous remediation.
Meanwhile, generative AI excels at analyzing unstructured data, summarizing threat intelligence reports, generating incident response playbooks, and even creating realistic phishing simulations for training.
Examples of AI in cybersecurity
Here’s how different forms of AI are being applied to solve critical security challenges across various industries.
Autonomous Threat Remediation in Finance
- Pain point: A financial services firm faces a sophisticated credential stuffing attack where bots are attempting to log in to thousands of customer accounts using stolen passwords. The security operations center (SOC) is flooded with thousands of individual alerts, making a coordinated response slow and overwhelming.
- How AI helps: An AI agent monitors login attempt patterns across the entire platform. Detecting the distributed, high-velocity nature of the attack, it recognizes the activity as a coordinated campaign.The agent autonomously initiates a workflow: it blocks the originating IP ranges, forces a multi-factor authentication (MFA) reset for all flagged accounts, and creates a high-priority incident within the organization’s preferred alerting or incident management platform. This allows it to provide a summarized report of its actions for the on-call security engineer.
Securing Medical IoT Devices in Healthcare
- Pain point: A hospital network contains thousands of connected medical devices, such as infusion pumps and patient monitors. A single compromised device could provide an entry point for a ransomware attack, jeopardizing patient safety and data. Manually monitoring the network behavior of every device is impossible.
- How AI helps: An AI agent continuously learns the normal baseline behavior for each type of medical device. When an infusion pump suddenly attempts to communicate with an unknown external server—a clear anomaly—the agent automatically isolates the device from the main network to prevent lateral movement. It then logs the event and alerts the biomedical and IT security teams with contextual details for immediate investigation.
Automated Vulnerability Management in Retail
- Pain point: A large e-commerce retailer discovers a critical zero-day vulnerability in a widely used open-source library that affects hundreds of its production microservices. The security team faces a frantic, manual race to identify, prioritize, and patch every affected system before it can be exploited.
- How AI helps: An AI agent subscribes to global vulnerability feeds. Upon learning of the new threat, it automatically scans the company’s entire code repository and running applications to identify all vulnerable instances. Based on predefined business context, the agent prioritizes the most critical customer-facing services and autonomously initiates the patching and deployment pipeline, compressing a multi-day manual effort into a few hours.
Incident report summarization for public sector agencies
- Pain point: A state-level transportation agency must comply with strict incident reporting regulations. After a security event, analysts spend hours manually compiling data from different logs (SIEM, firewall, endpoint) to write a detailed report for leadership and regulatory bodies.
- How AI helps: After an incident is resolved, a generative AI tool is fed all the raw, unstructured log data associated with the event. It automatically generates a clear, human-readable executive summary, a detailed timeline of events, an analysis of the attacker’s tactics and techniques, and a list of recommended remediation actions. This reduces reporting time from hours to minutes, ensuring accuracy and consistency.
Secure code generation for AI infrastructure
- Pain point: A company developing AI-powered services wants to accelerate its development lifecycle. Its developers use AI coding assistants to write code faster, but they risk unintentionally introducing security vulnerabilities, such as hardcoded secrets or insecure data handling practices.
- How AI helps: The company integrates a specialized generative AI model directly into the developers’ integrated development environment (IDE). This “security co-pilot” analyzes AI-generated code snippets in real time, automatically flagging potential security flaws and suggesting more secure alternative code. This shifts security left, catching vulnerabilities before they ever reach production.
Dynamic security awareness training in higher education
- Pain point: A university’s IT department struggles with generic phishing awareness campaigns that fail to engage students and faculty, who are often targeted by sophisticated, context-specific attacks.
- How AI helps: The security team uses a generative AI platform to create hyper-realistic and personalized phishing simulations. The AI can craft emails mimicking university communications, referencing specific departments, ongoing events, or even relevant academic topics. This provides a far more effective training experience, teaching users to spot the nuanced attacks they are most likely to face.
Benefits of using AI in cybersecurity
Integrating AI into security operations provides a powerful defense against modern threats.
- Enhanced threat detection and speed: AI algorithms can analyze billions of data points in real time, detecting subtle indicators of compromise that are invisible to human analysts.
- Automation of repetitive tasks: AI handles the tedious, high-volume work of sorting alerts and analyzing logs, freeing up security professionals to focus on strategic threat hunting and investigation.
- Reduced alert fatigue: By intelligently correlating and prioritizing alerts, AI ensures that human responders only focus on the incidents that truly matter, preventing burnout and reducing the risk of a critical threat being missed.
- Faster incident response: AI-powered automation can execute predefined playbooks to contain threats in seconds, dramatically reducing the mean time to resolution (MTTR) and minimizing the potential damage of an attack. This is a core component of modern security incident management.
- Proactive threat hunting: Machine learning models can predict potential future attacks by identifying precursor activities and emerging threat patterns, allowing teams to strengthen defenses before an attack is even launched.
Challenges to using AI in cybersecurity
Despite its power, implementing AI in security is not without its operational hurdles.
- Adversarial AI: Just as defenders use AI, attackers are using it to create more sophisticated attacks, such as deepfake phishing attempts or malware that can evade AI-based detection models.
- Data quality and bias: AI models are only as effective as the data they are trained on. Incomplete, inaccurate, or biased training data can lead to poor performance and an increase in false positives or negatives.
- Model explainability: Many complex AI models operate as “black boxes,” making it difficult for security analysts to understand why a particular decision was made. This can be a major challenge during forensic investigations and for regulatory compliance.
- Integration complexity: AI security tools must be seamlessly integrated into a company’s existing security stack (SIEM, SOAR, EDR). Poor integration can create data silos and operational friction, undermining the benefits.
- System reliability and uptime: When an AI system is responsible for critical functions like threat detection or automated response, its availability is non-negotiable. An outage or performance degradation in your security AI is a critical incident in itself, creating a dangerous blind spot.
The AI-powered tools that form your modern digital immune system are built on complex, distributed infrastructure. When these systems fail—whether it’s a data pipeline feeding your threat detection model or an API for your automated response agent—your organization is left vulnerable. A missed alert isn’t just a technical glitch; it’s a security gap that attackers can and will exploit.
The PagerDuty Operations Cloud is essential for managing this new layer of operational risk. By centralizing signals from across your AI and security stack, PagerDuty ensures that the right teams are engaged instantly when an issue arises with your critical defense systems. In an era where cyber threats are AI-driven, your defense must be AI-powered and, above all, operationally resilient.
Learn how to build a more secure and resilient future with PagerDuty’s AI solutions.