Best Practices to Succeed at Continuous AWS Security Monitoring

This article will look at best practices for how organizations can efficiently ingest, normalize, and structure their AWS logs so that security teams can effectively implement the proper detections for their specific AWS environment. We'll also discuss how leaders can enable a Detection-as-Code practice empowering security teams to scale their security engineering operations resiliently alongside their AWS environment as it changes and grows.

The Current State of Security Log Monitoring

As businesses move more of their operations to the cloud, the need for robust security log monitoring becomes increasingly important. Security log data can provide valuable insights into an organization's IT infrastructure and help identify potential security threats.

However, many businesses struggle to utilize log monitoring in the cloud fully and are often bewildered by the complexities and scale of available logs in their cloud environment. With the multitude of AWS-specific tools and services available, the set of logs generated by these chosen services can add up quickly.

In a recent Panther survey of security professionals who protect an AWS environment, 18.8% of respondents indicated they log data from more than 40 accounts, and over 54.4% say their environments are "very complex." In addition, 64.8% of these respondents said their companies have "only existed in the cloud." And a plurality (17.9%) said collecting large amounts of log data from multiple sources quickly was their top challenge. 

This complexity is a shame because security log data is essential in identifying and mitigating cyber threats. By tracking activity in your environment and detecting any suspicious behavior, you can reduce the risk of a data breach or other security incident. Here are some best practices on how to do just that.

1. Efficiently Ingest, Normalize, and Centralize AWS Logs

One of the best ways to protect your data and ensure the security of your AWS environment is by efficiently ingesting, normalizing, and centralizing your logs. Doing so lets you comprehensively view all activity in your environment and quickly detect any suspicious behavior.

Organizing and centralizing AWS logs can be difficult for security practitioners, but it is necessary to have visibility across your environment. Unfortunately, logs are siloed in AWS, creating a problem of having too many uncorrelated logs, and this lack of correlation means a lack of visibility and context. To gain back this visibility, we suggest you centralize your AWS logs with other relevant security details in one place.

Unfortunately, when centralizing your AWS logs with a legacy SIEM solution, you are opening yourself up to being charged ridiculously high prices. As you scale, the price of managing these logs can climb very quickly and become expensive. Therefore, security teams must find a cost-effective platform that will scale well with a growing AWS footprint and perform quickly across large amounts of log data. 

By efficiently ingesting, normalizing, and centralizing your AWS logs, you can gain a deeper understanding of how your environment is being used and help identify potential security threats. Implementing these measures will help ensure your data's safety and your AWS environment's integrity.

2. Implement the Right Detections for Your Environment

Another critical step in protecting your data and ensuring the security of your AWS account is to implement good detections. This means choosing the right detection methods and settings for your specific environment.

Security practitioners need an easy way to implement out-of-the-box detection coverage aligned to best practice security frameworks like CIS and MITRE. However, once foundational coverage is in place; organizations also need the flexibility to implement custom or environment-specific detections.

To secure your AWS environment, it's crucial to use out-of-the-box detections and policies. First, doing so makes getting started easy. Then, leverage MITRE ATT&CK Mapping visualization to help understand the detections you need. Lastly, implementing detection logic using a general language instead of a convoluted, vendor-specific one. For example, Python is an expressive language that has been widely adopted by engineers of all stripes. Given the adoption and the robust set of libraries available for Python,  it is both simpler and more powerful for editing or writing custom detections to fit your particular AWS environment.

Implementing the right detections is an essential step in ensuring the security of your data and AWS environment. Choosing the proper methods and settings can reduce the risk of a data breach or other security incident.

3. Implement Detection-as-Code to Help Security Engineering Operations Scale and Adapt Alongside AWS

AWS infrastructure and services are flexible and scalable, so detecting threats should be too. To protect your data and ensure the security of your AWS environment, it's important to use code to define your detections rather than manual methods or rule-based systems. 

Detection-as-code is for writing detections as infrastructure-as-code (IaC), and configuration-as-code (CaC) is for machine-readable definition files and models for framing infrastructure. Detection-as-code is a systemized, adaptable, and all-encompassing way to detect threats using the software. It will improve the resilience of your security operations in the face of the ever-changing nature of AWS.

Security practitioners need a solution that can quickly ascertain which detections are running, what version of logic they're using, and how to update them without causing more problems. Implementing detection-as-code can help improve the accuracy and scalability of your detection operations. It also helps ensure that your detection methods are always up to date with the latest changes in your environment.

By using detection-as-code, teams can effectively manage their detection versions and understand which logic is used for each. In addition, this process makes it easier for the security team to readily use and adapt existing code for new AWS services rather than starting from scratch each time.

By implementing detection-as-code, you can improve the accuracy and scalability of your detection operations because it allows you to test your detection methods using actual data instead of hypothetical scenarios. This way, you can be sure your new detection strategy won't result in a glut of false alarms.

Conclusion

AWS is a rapidly growing platform, and the future of security log management looks bright for those who follow these best practices. Implementing the right detections, using detection-as-code, and adapting to changes in your environment are all essential steps in ensuring the security of your data and AWS account. In addition, following these best practices can help protect your organization from data breaches and other security incidents.