Role of AI in cybersecurity: use cases, benefits, and tools

AI is everywhere. Some embrace it, others fear it, and debates over its risks and benefits won’t end anytime soon. However, there are areas where AI’s adoption is met with little resistance – cybersecurity is one of them.

The reason is clear: the stakes are too high to ignore the advantages this technology brings.

Businesses, governments, and individuals rely on digital systems more than ever. Smart devices, cloud platforms, and online services make life more convenient, but they also expose sensitive data to cybercriminals. Attacks are getting more sophisticated, automated, and frequent, making traditional defense methods struggle to keep up.

That’s why AI is becoming a key part of cybersecurity, and our article is dedicated to this. We’ll analyze how AI is changing the way we fight cyber threats and learn how AI can protect your organization in an increasingly connected and vulnerable world.

AI in cybersecurity defined: what AI technologies are used in cybersecurity?

AI in cybersecurity refers to the practical application of artificial intelligence technologies to analyze and interpret vast amounts of data from multiple sources. It identifies patterns, detects anomalies, and provides actionable insights that help security teams investigate, respond to, and report on threats.

But before diving into its application, let’s first define what we actually mean by AI in cybersecurity. Several technologies fall under the AI umbrella. Here are some of the most common ones:

• Machine learning. ML allows cybersecurity systems to learn from experience. Instead of relying on fixed rules, it continuously analyzes patterns in network traffic, user behavior, and attack methods to spot potential threats.
• Deep learning. This is a more advanced form of ML that uses neural networks to recognize complex attack patterns. It’s especially useful for detecting malware that constantly changes its code to avoid detection.
• Neural networks. Neural networks mimic how the human brain processes information, helping cybersecurity systems recognize even the smallest signs of an attack. They are great at reducing false alarms by filtering out harmless activity from real threats.
• Large language models (LLMs) & generative AI. Still new to cybersecurity, LLMs are starting to prove useful in making sense of overwhelming amounts of security data. They can pull key insights from threat reports, logs, and intelligence feeds, helping analysts understand risks faster.

Use cases of AI in cybersecurity

Now that we’ve explored the technologies powering AI in cybersecurity, let’s talk about how they’re applied in real-world scenarios.

Identity and Access Management (IAM)

Every security system starts with the same question: who are you, and should you be here? AI helps answer this by analyzing sign-in patterns and detecting unusual behavior.

If an employee usually logs in from New York at 9 AM but suddenly tries to access company data from a new device in another country at midnight, AI flags it instantly. It can also enforce multi-factor authentication (MFA), requiring extra verification before granting access. In other words, AI makes sure the right people get in – and the wrong ones stay out.

Cloud security

Businesses today rely heavily on cloud platforms, often working with multiple providers simultaneously. They store sensitive data, run applications, and collaborate across multiple platforms.

With AI, you gain full visibility into risks and vulnerabilities across your multicloud estate. The technology helps monitor cloud activity, detecting unauthorized access, and identifying misconfigurations that could expose critical information.

Information protection

Sensitive data is everywhere – in emails, shared folders, cloud storage, and internal databases. The problem is that not everyone knows exactly where their critical information is or who has access to it.

AI helps security teams identify and classify confidential data, ensuring it’s properly protected. If an employee accidentally tries to send sensitive files outside the company, AI can block the action or raise the issue to the security team, and prevent a potential data leak before it happens.

Endpoint security and management

Organizations are packed with connected devices – laptops, phones, tablets, servers – each a potential entry point for cyber threats. Keeping track of them all is a challenge.

AI helps by automatically identifying every device on the network, monitoring their activity, and detecting signs of compromise. If an endpoint is behaving strangely – like transferring large amounts of data at odd hours – AI can flag it, isolate it, or notify security teams before an attack spreads.

Cyberthreat detection

Cyberattacks aren’t just increasing – they’re evolving. Hackers use automation, social engineering, and advanced malware to bypass traditional defenses.

AI-powered security tools like Extended Detection and Response (XDR) and Security Information and Event Management (SIEM) platforms analyze vast amounts of security data, spotting threats that might otherwise go unnoticed. Instead of waiting for an attack to be obvious, AI predicts risks, prioritizes alerts, and helps security teams focus on the real threats.

Incident investigation and response

When a security incident happens, every second counts. But sorting through endless logs and alerts can slow things down.

AI accelerates incident response by automatically analyzing attack patterns, correlating relevant events, and identifying the most critical pieces of information. Instead of spending hours manually searching for the root cause, security teams get clear insights instantly, helping them contain threats faster and reduce damage.

While AI is transforming cybersecurity, its adoption comes with challenges. High costs, lack of in-house expertise, and the complexity of integrating AI into existing systems can slow implementation.

For example, setting up AI-driven threat detection tools like XDR or SIEM requires upfront investment and skilled personnel. Similarly, training AI models to fit specific business needs can be resource-intensive.

Despite these challenges, the benefits of AI in cybersecurity far outweigh the hurdles.

Benefits of incorporating AI into cybersecurity

The ultimate goal of cybersecurity is to keep systems, data, and users safe from ever-evolving threats. Can AI ensure absolute protection? Probably not – no system is 100% attack-proof.

But AI significantly strengthens defenses by detecting, analyzing, and responding to threats faster than any human team could. Here’s how AI helps create a more secure digital environment:

1. AI detects critical threats in real time

Cyberattacks don’t wait, and neither should security teams. AI scans massive amounts of data at lightning speed, recognizing suspicious activity and flagging high-risk threats before they escalate. This early warning system gives organizations a crucial edge in stopping attacks before damage is done.

2. AI automates and simplifies security reporting

Security teams deal with endless logs, reports, and alerts. AI cuts through the noise by summarizing key findings, generating clear reports, and providing actionable insights. Instead of drowning in raw data, analysts get the information they need – fast.

3. AI uncovers hidden vulnerabilities

Hackers look for weak spots, and so does AI. By continuously analyzing systems, applications, and user behaviors, AI helps identify security gaps before they can be exploited. Whether it’s an outdated firewall or an unnoticed misconfiguration, AI ensures that weak points are found and fixed in time.

4. AI supports security teams in skill development

AI doesn’t just strengthen cybersecurity – it also helps analysts grow. By automating repetitive tasks, AI frees up security professionals to focus on more complex investigations. It also provides real-time recommendations, allowing teams to learn from AI-driven insights and improve their expertise.

5. AI delivers deep cyberthreat intelligence

Understanding the enemy is half the battle. AI continuously analyzes global threat patterns, learning from past attacks to predict and prevent future ones. By providing security teams with rich, data-driven insights, AI helps organizations stay one step ahead of cybercriminals.

Where can businesses get AI-powered security?

By now, it’s clear that AI is a powerful ally in cybersecurity. But one big question remains: Where do businesses actually get it?

AI is already integrated into many security tools used by businesses today. Organizations can either adopt ready-made AI security solutions or develop custom AI models to fit their needs.

1. AI-driven threat detection and response

These tools continuously monitor network activity, detect suspicious patterns, and respond in real time. Examples include:

• Microsoft Defender for Endpoint – Uses AI to detect and respond to advanced cyber threats on devices.
• IBM QRadar XDR – An extended detection and response (XDR) platform that combines AI and automation to identify security incidents across an organization.
• AWS GuardDuty – A threat detection service that analyzes AWS accounts, workloads, and data for malicious activity using machine learning.

2. Securing Internet of Things (IoT) devices with AI

IoT devices – smart sensors, cameras, and industrial equipment – are often targeted by cybercriminals. AI enhances security by monitoring unusual device behavior and blocking unauthorized access. Notable tools include:

• Azure Defender for IoT – Microsoft’s AI-driven tool for monitoring connected devices in industrial and enterprise environments.
• Darktrace Antigena – Uses AI to detect and stop cyber threats across IoT networks automatically.

3. AI-powered cloud security solutions

With businesses storing vast amounts of data in the cloud, AI helps identify vulnerabilities and block attacks before they happen. Some key cloud security tools include:

• Google Chronicle Security Operations – An AI-powered platform that scans cloud environments for cyber threats.
• Amazon Macie – Uses AI to identify and protect sensitive data stored in AWS.
• Palo Alto Networks Prisma Cloud – An AI-based security solution that detects cloud misconfigurations and suspicious activity.

4. AI-driven network intrusion detection and prevention

AI improves traditional firewalls and intrusion detection systems by analyzing network traffic in real time and stopping potential attacks before they cause damage. Examples include:

• Cisco SecureX – AI-driven threat detection and response for enterprise networks.
• Snort by Cisco – An open-source AI-enhanced intrusion prevention system that detects malicious traffic.
• IBM Watson for Cyber Security – Uses AI to analyze network threats and recommend response actions.

5. Custom AI security solutions

While ready-made tools are effective, some businesses – especially large enterprises with unique security needs – opt for custom AI models tailored to their specific environment. These solutions require data scientists and security experts to develop AI models that detect threats based on an organization’s unique risk landscape.

Conclusion

AI is a practical, powerful tool for cybersecurity. It enhances threat detection, automates responses, and helps prevent cyberattacks before they happen.

This is especially crucial in industries that handle sensitive information, such as healthcare, finance, government, and eCommerce, where data breaches can have serious consequences.

If you want to explore how AI can strengthen your business’s security, our experts are ready to help. Contact us for a consultation.