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AI and machine learning have been getting a lot of buzz in the last few years, largely for their roles in chatbots and content generation. However, machine learning and one of its subtypes, Natural Language Processing (NLP), are also powerful tools for application security.

To keep up with emerging threats, organizations need to have the best possible security tools at their disposal. Solutions that leverage NLP detect more vulnerabilities and threats than more traditional solutions. Additionally, NLP can improve threat intelligence and facilitate security policy enforcement. While the technology is not perfect, it is a valuable addition to your security toolbox.

NLP for Intelligent Vulnerability Detection

Natural Language Processing is an important development in machine understanding and generating human language. By combining statistics and linguistics, computer scientists have been able to design artificial intelligence that can interact successfully with human speakers. This is useful to people who regularly use digital assistants, but it also has important ramifications for application security.

Although there are myriad uses for NLP, one of the most important is its role in next-generation application security. It can be used to help detect vulnerabilities through pattern analysis, and it is able to synthesize larger amounts of data in less time than a human security professional.

Two important use cases for NLP include:

* Analyzing code comments and documentation. This can be challenging, but NLP is able to understand and respond to the material presented. Older technologies have been limited to analyzing the code itself, but NLP can read and respond to annotations and notes created by developers that are not part of the executable code.

* Contextual understanding of code semantics. NLP has the capacity to understand the context in which your application is built. It can account for how the application runs and the processes it follows as well as the available code.

Because NLP has a more nuanced and human-like concept of the application than traditional vulnerability testers, it will detect bugs that might evade an older solution. This is important for building a fully functional, reliable app, but it also improves the application’s security and reduces the risk of successful attacks after release.

Enhancing Threat Intelligence with NLP

NLP’s utility is not limited to the development process. It is also useful for improving security following the completion and release of your application. All applications, no matter how well they have been developed, are under threat once they are exposed to the web.

Processing and correlating unstructured security data

After an application’s release and once customers have begun to use an application, it’s a fair bet that malicious actors will also begin looking for potential vulnerabilities or ways to exploit business logic.

Enhancing threat intelligence

Threat intelligence solutions aggregate and analyze data from various types of activity around your application. Typically, security teams receive alerts when there is suspicious or atypical activity, and the potential attack type may be identified. NLP takes this a step further.

Traditional threat intelligence solutions are adept at identifying known attack patterns, but they do not always catch zero-day attacks. In contrast, NLP is able to evaluate activity based on typical patterns, context, and behavior analysis to determine whether unusual activity poses any risk.

Prioritizing and extracting IOCs

Another advantage that NLP offers is automated extraction of Indicators of Compromise (IOCs) from security reports. One of the challenges that security professionals face is a deluge of security data and alerts with few indications of priority or risk. NLP is able to sort through these alerts and aggregate those that indicate an urgent issue.

IOCs could be anything from a spate of unusual traffic to a login from an unexpected location. However, these things can occur without necessarily being part of an attack, and NLP can use other behavior around your application to put the IOC in context. If it appears to be a true attack, your security team can respond quickly, reducing your downtime and limiting the damage from an attack.

NLP-Powered Security Policy Management

Vulnerability detection and threat intelligence are both important security tools, but they work best in concert with enforced and effective security policies. One of the greatest threats to organizational security (and an often-used vector of attack) is human error. To secure applications against this vulnerability, many organizations have implemented zero-trust, enforce access controls, use monitoring tools, and provide training to employees.

All of these strategies are highly effective, especially when used together, but they can be further enhanced by NLP. NLP can act as an interface between human policy creation and your security tools’ response. By translating between human language and code, NLP assists security professionals with creating, managing, and enforcing policies.

Automated policy translation and enforcement can be much more precise and nuanced when informed by NLP. NLP is able to process words based on their use in human language, which provides insight into the intent and desired outcomes of those policies. Enforcement can then be done according to intent rather than strictly according to what is encoded.

As machine learning and natural language processing develop, they are valuable tools for next-generation application security solutions. Leveraging NLP especially enables security solutions to take a more proactive and adaptive approach to security. Rather than relying on strict code instructions, NLP is a flexible tool that can enhance security during development, threat intelligence, and policy enforcement after deployment.