Home Learn AI in Cybersecurity How Is AI Helpful in Cybersecurity? Advancements in AI have revolutionized many industries. Request demo AI in Cybersecurity Overview Analysts Benefits Challenges & Limitations Hackers Related Terms Resources Overview Why companies are using AI for cybersecurity, how it reduces risk – and what you should know about its challenges Is AI going to take over cybersecurity? It can seem that way. As human experts struggle to keep pace with intensifying threats, AI makes it possible to spot anomalies at lightning speed, identify threats quickly and accurately, respond automatically, and take a more proactive and preventive approach to protection. In fact, the real question might be whether modern cyber security is possible without AI. We’ll examine the essential role of AI in cybersecurity, how it’s used, the benefits it enables, and the challenges it can involve. Analysts How Analysts Use AI in Cybersecurity The complex and constantly changing nature of today’s hybrid environments can leave security teams drowning in data from myriad sources – network appliances, cloud providers, container orchestration platforms, user behavior logs, the list goes on. As noise and false positives increase, it’s hard to detect a real threat in time or respond quickly enough to stop an attack. Analysts are stuck in reactive mode, always one step behind attackers. AI helps SecOps teams overcome these challenges by delivering transformative value for key cybersecurity use cases. Threat Detection and Response Trained to tell the difference between normal and malicious activity, machine learning models can quickly detect signs of a threat in security data – including zero-day threats that traditional signature-based methods miss. AI security copilots with integrated threat intelligence feeds and frameworks can guide analysts through fast, effective response workflows to mitigate the threat. Anomaly Detection Machine learning also can help SecOps teams detect non-malware threats such as data exfiltration, advanced persistent threats, DDoS attacks, account takeovers, credential stuffing, and social engineering attempts. While traditional anomaly detection methods tend to produce a high volume of false positives, AI cybersecurity tools can use adaptive learning to become increasingly accurate over time and catch subtle patterns that humans would miss. Incident Response In addition to helping security teams work more quickly, AI cybersecurity tools can take initiative with their own real-time incident response steps – for example, by: • Isolating affected systems or blocking malicious IP addresses to contain the threat • Resetting compromised credentials • Prioritizing incidents based on severity and potential impact • Orchestrating response actions across security tools and platforms Automated actions like these can both speed threat response and ease the burden on human security teams. Reporting Security analysts need to stay focused on security – not writing and distributing reports. Generative AI-powered cybersecurity tools can process and analyze huge amounts of diverse security data into easily understandable summaries tailored to specific stakeholder personas. -Provided with up-to-date information, predictive insights, and recommended next steps, security and business leaders can make better decisions to manage risk and protect the organization. Benefits Key Benefits of AI for Cybersecurity Stopping Evolving and Zero-Day Threats Cyber criminals continuously develop new threats and modify existing ones to evade detection. Rather than relying on signature databases that quickly become outdated, machine learning models identify the deviations that can signal an attack of any kind. Enabling Predictive and Proactive Defense The best time to stop an attack is before it happens. By analyzing vast amounts of historical and real-time data, AI can predict future attack patterns and emerging threats so organizations can take steps to reduce their vulnerability and strengthen their security posture. Responding Sooner and Faster The speed of AI-powered cybersecurity tools makes it possible to monitor even extensive and complex environments in real time. By identifying potential threats earlier in the attack cycle, and enabling a faster response through automated actions and copilot-assisted workflows, they improve the chance of preventing significant damage. Improving Efficiency and Scalability Cybersecurity is getting more demanding – but security teams aren’t necessarily getting larger. AI can be a force multiplier for overstretched security organizations. By automating routine tasks, providing insights, generating reports, and making recommendations for next steps and best practices, AI cybersecurity tools make it possible for analysts to stay on top of an ever-changing threat landscape. Challenges & Limitations Challenges and Limitations of AI in Cybersecurity AI isn’t perfect, of course. As organizations integrate AI into their cybersecurity strategy, they need to be realistic about its limitations and the challenges it can pose. These include: Data quality and quantity – AI systems depend on large amounts of high-quality data to work effectively. It can be hard for organizations to pull together enough clean, relevant data to train their AI models properly. Integration complexity – Integrating AI into your security infrastructure and adapting its algorithms to work with your legacy systems can be a major project. Careful planning is key for a smooth, successful transition. Reliability and trust issues – AI is famous for humorous hallucinations, but security errors are no laughing matter. Many stakeholders may be understandably skeptical, and the “black box” nature of some algorithms can further erode trust. Make sure your system is accurate and dependable – and can prove it. Ethical concerns – Organizations need to make sure that AI tools aren’t capturing sensitive personal information, showing bias in the users or activities that trigger alerts, or excessively monitoring private activity. Skills – AI can greatly alleviate the shortage of security talent, but its implementation and management require expertise as well. SecOps teams will need new skillsets to use these tools effectively. Regulatory compliance – Organizations already face myriad rules governing the way they use data. With AI-specific regulations still a work in progress, it can be difficult to figure out how existing requirements apply to these systems. Staying on top of emerging rules will be essential to manage risk. Hackers How Hackers Use AI in Cybersecurity If security professionals hoped AI could be a key ally in the battle against cyber crime, we have bad news: It’s working for the bad guys, too. To cite just a few examples: Scale and automation – Hackers are using AI-powered tools to scan for vulnerabilities across vast networks and launch coordinated attacks on multiple targets simultaneously. Personalized attacks – AI can produce wonderfully convincing phishing and social engineering campaigns leveraging large datasets of personal information and communication patterns. Advanced malware – AI algorithms can help malware adapt and improve its behavior in real time to avoid detection and improve its success rate. Password cracking – Machine learning algorithms can analyze patterns in known passwords to generate more effective guesses, greatly reducing the time needed to breach accounts. Synthetic media – Deepfakes such as AI-generated voice or video impersonations can easily manipulate victims into divulging sensitive information or authorizing fraudulent transactions. Make no mistake: AI has become the latest arms race in cyber security. SecOps teams have no choice but to harness its power – because their adversaries already do. Related Terms What is Ransomware Protection? Ransomware protection is the process of preventing the occurrence of a ransomware event, and/or mitigating the risk of a successful attack. Learn more What is Cyber Deception? Cyber deception is a proactive security and defense tactic which hinges on deceiving bad actors and malicious attacks. Learn more What is Cleanroom Recovery? Cleanroom Recovery is a specialized data recovery process that ensures the secure and reliable retrieval of critical information from damaged storage devices in environments where data contamination poses a significant risk. Learn more related resources Explore related resources View all resources Checklist Cyber Recovery Readiness How to prepare, identify threats, assess the impact on business operations, and restore quickly. Report Cyber Recovery Readiness Learn hard fought lessons about data security, recovery, and cyber resilience from 1,000 global leaders in the inaugural Cyber Recovery Readiness Report from Commvault and GigaOm. Blog Guiding Principles for Responsible Artificial Intelligence AI has become an omnipresent force in how we communicate and in the services we use.