6 Real-life Use Cases & Leading Tools [2025]

AI intrusion prevention systems (IPS) use machine learning algorithms and behavioral analytics to detect and prevent several cyber threats. These systems integrate AI with traditional IPS technologies, improving their ability to detect and mitigate attacks in real time. AI IPS can:

• Proactively identify potential threats by analyzing patterns.
• Automate threat response actions such as isolating compromised endpoints.
• Improve accuracy by using contextual analysis and ML to reduce false positives.

1. Automated phishing response

AI IPS continuously monitors email inboxes for any reports of phishing attempts or suspicious emails. After detecting a potentially malicious email, AI IPS can present the analyst with actionable findings related to email phishing attempts, including:

• The user who reported the fraudulent email.
• The user who sent the email.
• IOCs such as URL, IP, and domain name.

Based on the analysis, AI IPS can take immediate action, including:

• Isolating affected endpoints: If an endpoint is suspected of being compromised, the AI IPS isolates the device from the network to contain any potential threats.
• Deleting malicious emails: Automatically removing the detected phishing emails from user inboxes, preventing further exposure.

For example, Cato’s IPS uses an AI-based inspection engine to analyze network domains, providing security teams with detailed information on phishing attempts. It detects domain squatting algorithms (DGAs) that attackers use to block someone else from registering a domain.

2. Network security monitoring 

AI IPS solutions monitor network traffic to detect and prevent threats such as malware, ransomware, phishing, and distributed denial of service (DDoS) attacks.

For example, Splunk or Vectra.ai uses AI algorithms that run on large volumes of data collected at different nodes of the network. This enables continuous monitoring, allowing these systems to detect and respond to network security threats in real time.

Real-life example

A large real estate company uses AI-driven network monitoring across its cloud, data center, IT, and IoT networks for threat hunting.

After deploying an AI-powered IPS solution, the company gained real time context and insights into threat behaviors, reducing the volume of alerts. With only 2-3 actionable alerts per day, the security team could focus on investigating high-priority incidents.

3. Ransomware detection and mitigation

AI IPS detects unusual encryption activities or the rapid spread of malicious files across the network, automatically isolating infected devices to prevent ransomware from encrypting critical patient records. 

Real-life example

Omada Health, a California-based digital health company, implemented an AI-driven IPS to protect sensitive patient data from ransomware attacks.

By deploying the AI IPS, Omada Health enhanced its ability to detect ransomware attacks early, isolating affected systems and minimizing the risk of data loss or encryption. This proactive defense helped maintain patient data integrity.

4. Securing industrial control systems

AI IPS detects and blocks attempts to exploit vulnerabilities in industrial protocols, ensuring the integrity and availability of critical infrastructure components. 

Real-life example

Corix, a utility company, utilized an AI-driven IPS to protect its industrial control systems (ICS) from cyber threats. Corix:

• Detects unusual trends in data flows
• Blocks attackers’ attempts to travel within the ICS network.
• Implements real time protective steps, such as isolating infected devices.

5. Advanced persistent threat (APT) detection and prevention

An advanced persistent threat (APT) is a stealthy cyber attack (e.g. stealing confidential information) in which an intruder gains access to a network and remains undetected for an extended period.

By aggregating data from networks, endpoints, the cloud, and application environments, the AI IPS can detect advanced persistent threats (APTs). The AI IPS system can continuously monitor for unusual activity or lateral movement, which are common indicators of APTs. After detecting such behavior, the AI IPS can take immediate action, such as blocking suspicious traffic and isolating compromised endpoints.

For example, Vectra’s AI Platform uses automated AI-driven detections focused on the techniques APTs deploy to move laterally across identity, public cloud, SaaS, and data center networks.

6. Automated integrations

AI IPS collaborates with existing security systems to increase threat detection by utilizing middleware or APIs to facilitate communication and data exchange between various systems. This enables, analysts to handle threats without the need for scripting and perform remediation operations such as network quarantine or automated policy enforcement in cloud environments.

IPS providers include both hardware appliances and several types of software solutions, as well as open source and commercial technologies.

Commercial IPS tools:

• Cisco integrates IPS protection into its firewall appliances, which are featured in products like Cisco Secure IPS, which uses malicious file/behavior detection algorithms. By analyzing file traffic and system behavior, Cisco Secure IPS can detect suspicious patterns, such as unusual file behavior or unauthorized access attempts.
• Palo Alto Networks integrates IPS components into its threat protection products, which use AI-based network traffic analysis to provide a deep insight into network patterns and anomalies.

Open source IPS tools:

• Some IPS providers perform this security function using extended detection, response, (XDR) and endpoint protection. For example, Atomic OSSEC combines hundreds of additional OSSEC rules with ModSecurity web application firewall rules to form a single extended detection and response (XDR) solution.
• Some open source IPS tools like Suricata focus on detecting attacks using identified signatures. However, Suricata also offers AI framework integrations that can automatically generate new signatures based on evolving attack patterns.

For more details read our article on top IDS/IPS & open source alternatives.

Why should SOC teams use AI IPS?

AI IPS enhances SOC efficiency, reduces workload, and ensures effective threat detection and mitigation, AI IPS can:

• Reduce noise and focus on key alerts: Reduce noise by filtering and prioritizing actionable alerts, allowing analysts to focus on potential threats that matter most.
• Streamline threat detection and response: Enable SOC teams to detect, respond to, and remediate threats across multiple attack channels, including email, endpoints, networks, and the cloud. This helps eliminate the inefficiencies of switching between multiple-point solutions.
• Automate time-consuming tasks: Automate repetitive but essential tasks, and free up analysts to concentrate on complex investigations, improving overall SOC productivity and response times.
• Simplify investigation and response: Codify investigation and response playbooks, guiding SOC teams through standardized processes and making it easy for even a less experienced analyst to take action to stop an attack.

This proactive strategy also enables these systems to have higher classification accuracy to detect previously unknown patterns and zero-day vulnerabilities.

See the classification accuracy of AI IDS using machine learning and deep learning:

Source:

Note that, AI IPS is less accurate with new attacks that lack historical signatures or behavioral patterns, and those that use heavy encryption to mask their actions.

AI IPS threat prevention methods

When an IPS identifies a threat, it logs the event and sends it to the SOC, typically via a security information and event management (SIEM) tool. Then it automatically takes action to respond  to the threat using tactics such as: 

• Blocking risky traffic: An AI IPS can filter out malicious activity before it reaches other security devices or controls. Some IPSs can reroute traffic to a honeypot, a decoy asset to make attackers think they’ve succeeded when, in reality, the SOC is tracking them.
• Removing risky content: An AI IPS may enable communication to continue while filtering out risky information, such as discarding malicious packets or removing malicious files from an email.
• Activating other security devices: An AI IPS can update firewall rules to stop a threat or change router settings to activate other security devices.
• Enforcing security policies: Some AI IPS can prevent attackers and unauthorized users from violating enterprise security policies. For example, if a user attempts to transfer sensitive information from a database where it is not permitted, the IPS will deny it.

How does IPS differ from IDS?

An intrusion detection system (IDS)’s primary function is to identify threats and send alerts. They are important for monitoring real time control systems, which need to function constantly and with high availability.

An intrusion prevention system (IPS) goes a step further, taking proactive and real time actions to prevent these threats from affecting the network or computing infrastructure. This quick response can help minimize the spread of malware throughout a network and prevent data breaches.

For more details read our article on intrusion prevention.

Further reading

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