AI intrusion prevention systems (IPS) use machine learning algorithms and behavioral analysis to detect and prevent several cyber threats. AI can enhance traditional IPS capabilities by enabling faster, more adaptive, and more cost-effective detection, especially for organizations with limited resources.
See AI IPS use cases with real-world examples and the top 4 AI IP tools.
AI IPS usage examples
AI IPS allows you to:
- Proactively identify potential threats by analyzing patterns.
- Automate threat response actions such as quarantining compromised endpoints.
- Reduce false positives and improve accuracy using contextual analysis and ML.
1. Automatic phishing support
AI IPS continuously monitors your email inbox for phishing attempts and suspicious email reports. After detecting potentially malicious emails, AI IPS can provide analysts with actionable findings related to email phishing attempts, such as:
- User who reported a fraudulent email.
- The user who sent the email.
- IOCs such as URLs, IPs, and domain names.
Based on the analysis, AI IPS can take immediate actions such as:
- Isolation of affected endpoints: If you suspect that an endpoint has been compromised, AI IPS isolates the device from the network to contain the potential threat.
- Delete malicious emails: Automatically delete detected phishing emails from users’ inboxes to prevent further exposure.
For example, Cato’s IPS uses an AI-based inspection engine to analyze network domains and provide security teams with detailed information about phishing attacks. Detects the Domain Generation Algorithm (DGA) used by attackers to prevent domains from being registered by others.
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 and Vectra.ai use AI algorithms that run on large amounts of data collected across various network nodes. This enables continuous monitoring, allowing these systems to detect and respond to network security threats in real time.
real example
A leading real estate company uses AI-driven network monitoring to hunt threats across its cloud, datacenter, IT, and IoT networks.
After implementing an AI-powered IPS solution, the company gained real-time context and insight into threat behavior and reduced alert volume. With only two to three actionable alerts per day, security teams can focus on investigating high-priority incidents.
3. Ransomware detection and mitigation
AI IPS detects anomalous cryptographic activity and the rapid spread of malicious files across your network, automatically quarantining infected devices and preventing ransomware from encrypting critical patient records.
real example
Omada Health, a California-based digital health company, deployed AI-driven IPS to protect sensitive patient data from ransomware attacks.
By deploying AI IPS, Omada Health strengthened its ability to detect ransomware attacks early, isolate affected systems, and minimize the risk of data loss or encryption. This proactive defense allowed us to maintain the integrity of patient data.
4. Ensuring the security of 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 example
Utility company Corix used AI-powered IPS to protect its industrial control systems (ICS) from cyber threats. Corix:
- Detect unusual trends in data flow
- Blocks attempts by attackers to move within the ICS network.
- Implement real-time protection steps, such as isolating infected devices.
5. Advanced Persistent Threat (APT) Detection and Prevention
Advanced Persistent Threats (APTs) are stealth cyberattacks (such as stealing sensitive information) in which an intruder gains access to a network and remains undetected for an extended period of time.
By aggregating data from your network, endpoints, cloud, and application environments, AI IPS can detect advanced persistent threats (APTs).
AI IPS systems can continuously monitor abnormal activity and lateral movement, which are common indicators of APT. When AI IPS detects such behavior, it can take immediate action, such as blocking suspicious traffic or quarantining compromised endpoints.
For example, Vectra’s AI platform uses automated, AI-driven discovery focused on the techniques APT deploys to move across identities, public clouds, SaaS, and data center networks.
6. Automatic integration
AI IPS works with existing security systems to enhance threat detection by leveraging middleware and APIs to facilitate communication and data exchange between different systems. This enables analysts to respond to threats without scripting and perform remediation operations such as network quarantine and automatic 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 firewall appliances in products such as Cisco Secure IPS, which use malicious file/behavior detection algorithms. By analyzing file traffic and system behavior, Cisco Secure IPS can detect suspicious patterns such as anomalous file behavior or unauthorized access attempts.
- palo alto networks integrates IPS components into its threat prevention products, using AI-based network traffic analysis to provide deep insight into network patterns and anomalies.
Open source IPS tools:
- Some IPS providers use Extended Detection and Response (XDR) and Endpoint Protection to perform this security function. For example, Atomic OSSEC combines hundreds of additional OSSEC rules and ModSecurity web application firewall rules to form a single Extended Detection and Response (XDR) solution.
- Some open source IPS tools, such as Suricata, focus on detecting attacks using predefined signatures. However, Suricata also offers AI framework integration that can automatically generate new signatures based on evolving attack patterns.
Benchmarks show that combining machine learning models with open source IDS/IPS tools such as Snort and Suricata can improve threat detection and log analysis. Among the models tested, random forests and decision trees performed best in terms of accuracy and speed, while logistic regression became less efficient on large data sets.
For more information, see our article on top IDS/IPS and open source alternatives.
Why should SOC teams use AI IPS?
AI IPS improves SOC efficiency, reduces workload, and ensures effective threat detection and mitigation. AI IPS allows you to:
- Reduce noise and focus on important alerts: Filter and prioritize actionable alerts to cut through the noise and help analysts focus on the potential threats that matter most.
- Streamline threat detection and response: Enabling SOC teams to detect, respond to, and remediate threats across multiple attack channels including email, endpoints, network, and cloud. This eliminates the inefficiencies of switching between multipoint solutions.
- Automate time-consuming tasks: Automate repetitive but important tasks, free up analysts to focus on complex investigations, and improve productivity and response time across your SOC.
- Simplify investigation and response: Systematize your investigation and response playbook and guide your SOC team through a standardized process, making it easy for even the most inexperienced analyst to take action to thwart an attack.
This proactive strategy improves the classification accuracy of these systems and allows them to detect previously unknown patterns and zero-day vulnerabilities.
Check the classification accuracy of AI IDS using machine learning and deep learning.
sauce:
Note that AI IPS is less accurate for new attacks that lack historical signatures or behavioral patterns, or for attacks that use strong encryption to hide their actions.
How to protect against AI IPS threats
When an IPS identifies a threat, it logs the event and sends it to the SOC, typically through a security information and event management (SIEM) tool. It then automatically takes action to respond to the threat using tactics such as:
- Block dangerous traffic: AI IPS can filter and eliminate malicious activity before it reaches other security devices and controls. Some IPSs can reroute traffic to honeypots, which are decoy assets that trick attackers into thinking the attack was successful, when in fact the SOC is tracking them.
- Remove dangerous content: AI IPS has the potential to filter out dangerous information while allowing communications to continue, such as dropping malicious packets or removing malicious files from emails.
- Enable other security devices: AI IPS can update firewall rules to stop threats and change router settings to activate other security devices.
- Applying security policies: Some AI IPS can prevent attackers and unauthorized users from violating corporate security policies. For example, if a user attempts to transfer sensitive information from a database for which they are not authorized, IPS will reject it.
How is IPS different from IDS?
sauce: Comparative study of AI models in open source IDS IPS
Ann Intrusion Detection System (IDS) Its main function is to identify threats and send alerts. These are important for monitoring real-time control systems that must be highly available and always functional.
Ann Intrusion Prevention System (IPS) goes a step further and takes proactive, real-time measures to prevent these threats from impacting your network and computing infrastructure. This rapid response can minimize the spread of malware throughout your network and prevent data breaches.
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principal analyst
Sem Dilmegani
principal analyst
Cem’s work has been cited by major global publications such as Business Insider, Forbes, and the Washington Post, global companies such as Deloitte and HPE, NGOs such as the World Economic Forum, and supranational organizations such as the European Commission. See more reputable companies and resources that reference AIMultiple.
Throughout his career, Cem has worked as a technology consultant, technology buyer, and technology entrepreneur. He has spent more than a decade advising companies on technology decisions at McKinsey & Company and Altman Solon. He also presented a McKinsey report on digitalization.
Reporting to the CEO, he led the communications company’s technology strategy and procurement. He also led the commercial growth of deep tech company Hypatos, from 7-figure annual recurring revenue and 0 to 9-digit valuation within two years. Cem’s work at Hypatos has been featured in major technology publications such as TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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