Consider a Data Breach

While security measures like data encryption secure data in the event of a breach, proactive monitoring for suspicious events—like changes in data access permissions—enables teams to identify and prevent threats before they escalate into incidents. This is why effective threat detection and response is a key part of maintaining full protection of an organization’s assets, data, and operations.

Modern Threat Detection for Modern Demands

With ever-changing business requirements and a constantly evolving threat landscape, security teams need agile solutions—threat detection that:

• Adapts to shifting priorities

• Scales with business needs

• Optimizes detection through automation and customization

• Remains highly accurate

Detection-as-Code: Flexible and Customizable Threat Detection

Detection-as-Code (DaC) is a modern approach to threat detection that enables security teams to write, manage, and deploy detections through code.

Like other "as-code" approaches in DevOps and DevSecOps, DaC offers:

• Version control for better collaboration and auditing

• Automation and scalability for efficient deployment

• Customization to close security gaps

• Improved workflows that enhance security posture

Most importantly, DaC allows teams to remain flexible, adapting to evolving business requirements and new threats.

How Does Detection-as-Code Work?

Detection-as-Code is integrated into modern SIEM software. A SIEM monitors applications and infrastructure for threats in real time, sends alerts when threats are detected, and enables teams to respond immediately. SIEMs achieve this by ingesting and aggregating logs, normalizing them, and analyzing them against detection rules.

A detection is a rule that defines when an alert should trigger based on certain conditions. For example, a rule could detect brute force attacks by monitoring for multiple failed login attempts followed by a successful login. The detection defines both the condition and the alert.

Modern SIEMs offer Detection-as-Code, allowing teams to write detection rules in code, such as Python, and manage them with version control. Like traditional SIEMs, these coded detections process all ingested log data in real time and generate alerts as needed.

Features of Detection-as-Code

With Detection-as-Code, the process of writing and managing detections is structured, yet flexible and customizable. Key features include:

• Python-based detections. Using Python or another widely-used programming language enables teams to extend built-in detections or create custom ones, ensuring full coverage.

• Version control. Teams can collaborate on detections, conduct peer reviews, roll back changes, and audit detection updates, ensuring consistency and transparency.

• Source data normalization. Detections work with any data source because data is normalized before being processed, allowing teams to develop comprehensive threat detection across all sources.

• Testing. Unit tests can verify the accuracy and reliability of coded detections before deployment.

• Automation. Coded detections can be integrated into CI/CD pipelines for automatic testing and reliable deployment.

• Reusability. Detections can be reused with new data sources or projects, reducing development time and improving security coverage.

How Detection-as-Code Improves Security Posture

A strong security posture minimizes risks and ensures an organization can respond efficiently to incidents. Detection-as-Code enhances security by:

• Reducing false positives. Customizable detections improve alert accuracy, minimizing noise and alert fatigue.

• Providing better security coverage. Automated, reusable, and customizable detections allow teams to focus on developing tailored threat detection.

• Reducing tool fragmentation. By working with any data source, Detection-as-Code eliminates the need for multiple security tools.

• Ensuring consistency. Codified detection rules are applied consistently across different environments, reducing discrepancies.

• Increasing transparency. The logic behind DaC detections is explicit, making it easier to audit, review, and comply with security standards.

• Improving agility. Security teams can quickly adapt to new threats by implementing changes faster.

• Encouraging collaboration. Developers, security teams, and operations can work together more effectively on detection rules.

• Driving innovation. The flexibility of DaC allows security teams to experiment with new detection methods and approaches.

Comparing Traditional SIEM vs. Detection-as-Code

In traditional SIEMs, detections process normalized and aggregated log data in real time, but they are created and managed differently:

• Detections are created through forms in a proprietary online portal, offering structure but lacking version control and collaboration.

• Some detections use a vendor-specific language, which can increase complexity and limit flexibility.

• Detections are managed within the portal, which may limit automation and transparency.

Detection-as-Code overcomes these limitations by offering version control, automation, and customization, allowing teams to respond to threats with greater efficiency and precision.

A Case Study: How Bitstamp Uses Detection-as-Code

More organizations are adopting Detection-as-Code to enhance their security programs. Bitstamp, one of the world's longest-standing cryptocurrency exchanges, has leveraged DaC to improve its security posture. By implementing coded detections, Bitstamp has enhanced its ability to monitor threats, reduce false positives, and scale its security operations effectively.

Detection-as-Code is transforming how security teams operate by providing a flexible, scalable, and efficient approach to threat detection. As cybersecurity threats continue to evolve, organizations that embrace DaC will be better equipped to stay ahead of attackers and protect their critical assets.

There‚'s more to learn about detection-as-code! Check out the case study of how Bitstamp uses Panther to accelerate its detection testing and deployment. You‚'ll learn about Bitstamp‚'s challenges in creating detections with a vendor-specific language, and how switching to use Panther‚'s Python-based detection-as-code accelerated their operations.

Curious about Panther? Request a demo.