Today, 94% of organizations are using cloud technology, and this swift evolution to the cloud means security teams are handling more data and more alerts than ever. Additionally, threats and attacks are only increasing in frequency — it’s estimated that a cyber attack occurs every 11 seconds — and sophistication. But more often than not, security teams are overwhelmed because they don’t have the right tools and approaches to handle modern threat detection at scale.
Security team leaders should have updated tools and approaches to help them protect their organization, and the best approach they can take is to adopt detection-as-code. Here’s more about detection-as-code and its benefits, as well as some best practices to help you gain success as you begin to use detection-as-code in your security approach.
The Benefits of Detection-as-Code
What if threat detection could be flexible, tailored to your organization’s needs, scalable, testable, and more? That’s what detection-as-code is: An approach to writing detections that employs a universal programming language and software engineering best practices.
Detection-as-code brings a number of benefits to your organization, starting with the ability for your team to write custom, high-quality detections that are tailored to what you need to be alerted to. By using a universal coding language like Python, you’re moving away from restrictive domain-specific languages. As you build detections, you can reuse the code across workflows without having to start from scratch. Detection-as-code allows you to automate your work as well, decreasing human error and streamlining response time. As you increase automation, your team can focus more on fine-tuning alerts. Finally, utilize version control systems to help you know which version of detection you’re currently working with or to revert to a previous version.
Ultimately, detection-as-code offers standardization, sustainability, and reliability and is a forcing function for security teams to automate by default, operate at a higher scale, and improve team efficiency. So how can you get better at it?
Four Best Practices
If you’re just starting to use detection-as-code or want to improve your approach, here are four best practices to help you grow and evolve your skills in leveraging detection-as-code for your organization.
1. Test, test, and Test Some More
One of the best things you can do as you build detections is to ensure that all of your detections are well-tested! Take a test-driven development (TDD) approach to your detection-as-code. You’ll discover blind spots early on, cover testing for false alerts, and evolve and improve your detection efficacy. Test for syntax and with real data to ensure you are able to check how these detections behave in the wild.
2. Learn Your Language
One of the advantages of detection-as-code is that it frees you from restrictive domain-specific languages to use one that’s more universal. Security practitioners are already familiar with Python as a scripting language, and basic Python can be learned by anybody. The best way to help improve your threat detection is to become skilled at writing in Python. This ensures that you are writing code in ways that are sustainable, correct, and aligned with other software developers, which makes it easier to collaborate with your team. Use CI tools to make this easier for you, such as formatters, linters, and more. Learn from your team’s detection requirements; over time, you’ll improve your intuition, improving your security game.
3. Adopt a CI/CD Mindset
Adopting a CI/CD mindset and responding more like software engineers can go a long way toward streamlining your detection processes. Continuously iterating on detection rules and alerts, testing those detections, and quickly deploying them not only allows you to be responsive in your improvements but allows you to leverage version control, unit testing, and other benefits. Above all, it allows you to leverage the power of automation in your workflows.
4. Get Creative With Code
Writing code allows you to be creative because coding is problem-solving and unlocks new ways of approaching old problems. As such, teams who are writing sophisticated detections can begin to test more advanced methods of analysis, such as statistics, machine learning, graphs, and more. However, increasing sophistication doesn’t have to mean increasing complexity, as writing tailored code for your detections will actually simplify your approach.
Modernizing Threat Detection
Detection-as-code is the approach that will elevate your threat detection and make your security team better prepared, more efficient, and more creative with their response. But be sure that you’re learning all the best practices around detection-as-code in order to make your efforts more pointed and successful.