As modern software and infrastructure have become more complex, organizations have started to adopt new strategies for IT operations and security. This includes observability to identify performance issues, and more recently, security observability and Detection as Code to improve cybersecurity. These practices leverage system data to help organizations maintain reliability, availability, and security in rapidly evolving digital environments.

In this article, we’ll discuss the emergence of security observability, how this has enabled detection as code, and the impact these practices can have on the security posture of modern systems. We’ll also cover how AI and automation will continue to impact security in the future.

The Emergence of Security Observability

Observability is a newer approach to system monitoring that site reliability and operations teams have started to adopt as modern applications and infrastructure have become more distributed and dynamic in nature. Traditional monitoring with predefined metrics was sufficient for monolithic applications deployed on static networks, but managing today’s systems requires deeper and more holistic data insights.

Modern observability tools track logs, metrics, and traces to provide comprehensive visibility into the overall system as well as how the individual components interact with each other. SRE teams can analyze this data — or even apply AI and machine learning algorithms — to uncover unknown issues before they impact users.

In recent years, many security teams have started adopting operational observability practices to better understand system behavior from a security perspective. This newer approach, typically referred to as security observability, enables security teams to analyze a wide range of data to streamline incident response, threat modeling, vulnerability management, and other security processes.

While logs and metrics have always been a key focus for security teams, newer observability tools have provided additional context. For example, tagging is a way to correlate logs to certain user behavior to investigate breaches, and memory usage is a useful metric to detect potential denial of service attacks. Traces haven’t been used as much by security teams in the past, but they’re a useful way to trace the source of vulnerabilities within distributed systems.

Observability data can help incident response teams track down the root cause of an incident and mitigate the impact of a breach. System data can also be analyzed to discover new vulnerabilities, which goes beyond investigating specific security incidents or monitoring known attack vectors. By leveraging security observability, organizations can enhance their security posture and proactively defend against potential threats.

What Is Detection as Code?

Infrastructure as Code (IaC) — which involves managing and provisioning computing resources using machine-readable definitions — sparked the “everything as code” trend, where IT teams are codifying most aspects of software delivery and infrastructure management. This includes a new security and threat detection approach called Detection as Code.

Detection engineering has become a critical cybersecurity discipline with a focus on designing and managing systems to detect malicious behavior. Manual threat detection processes cannot keep up with constantly evolving distributed systems, so many detection engineering teams are adopting Detection as Code to automate threat detection and response.

Many teams that embrace security observability are also adopting Detection as Code because these practices take advantage of logs and other common data sources. By applying threat detection rules to this data, Detection as Code tools can proactively identify and mitigate threats. Security teams can then scale these threat detection processes across all system components, even as the size and complexity of environments expands.

Detection as Code also integrates seamlessly into automated CI/CD pipelines, ensuring security measures are consistently applied as early on as possible. This means Detection as Code aligns with other trends like DevSecOps and shift-left security. As part of a CI/CD pipeline, threat detection rules can be automatically tested for quality assurance to minimize false positives and improve efficacy.

Since threat rules are defined in code, they can be stored in repositories with version control systems. Rules can be easily reused in different contexts, and quickly reverted to previous versions if there is an issue. This allows security teams to update detection rules based on emerging threats and deploy them across all environments in real time.

In short, Detection as Code allows security and detection engineering teams to create custom threat detection rules that can be automatically tested and applied consistently across different environments. This flexible and systematic approach bridges the gap between raw observability data and actionable security insights, leading to more robust and agile security.

Transforming Threat Detection with Automation & AI

The combination of security observability and Detection as Code significantly enhances an organization’s threat detection capabilities and overall security posture. By providing deeper insights into system behavior and enabling automated responses to threats, these practices help organizations stay ahead of potential security issues.

As the cybersecurity industry continues to evolve, however, it’s important for organizations to consider new ways to integrate emerging technologies into their security strategies. Embracing automation and AI can lead to more efficient and effective threat detection processes, and in turn, reduce the resources required to maintain an adequate security posture.

In the near future, AI and machine learning will likely play a crucial role in generating new threat detection rules and analyzing security observability data. AI algorithms can identify patterns and anomalies that might be missed by manual processes, enabling faster and more accurate threat detection. By automating these processes, security teams can focus on higher-level strategic initiatives that make a greater impact on overall security.

While security observability, Detection as Code, and AI can enable organizations to stay ahead of malicious actors, it can still be challenging to adopt these practices. That’s why it makes sense for many organizations to partner with a technology expert to develop a cybersecurity strategy and implementation roadmap.

AHEAD is an enterprise solutions expert that helps organizations navigate the complexities of modern digital platforms. Our platform engineering team can assist you in modernizing your software delivery process and infrastructure with IaC and observability, and our cybersecurity team can develop a security strategy that leverages emerging technologies like AI and Detection as Code tools.

Contact AHEAD today to learn more about modernizing your systems and enhancing your security posture.