Security incidents and cyber-attacks cost companies billions each year. Complex, distributed, or microservice-based applications make these incidents challenging to detect.
One critical component of operational security is the ability to detect and prevent cyberattacks in running applications. But, building and maintaining secure applications before attacks occur offers even more value. We can develop software that minimizes available vulnerabilities for attackers to exploit.
Observability plays a crucial role in detecting security vulnerabilities before they become an issue. This is because it allows us to see the state of our applications at any time.
Let’s explore the standard components that make a system more observable and five of the security vulnerabilities these structures can highlight. When we find these vulnerabilities in advance, we can patch them before hackers arrive.
Common Observability Components
We can integrate several observability components within our applications or systems. When we layer these components on top of each other, it improves any system’s observability. Before we dive into how we can use these components to find and patch specific vulnerabilities, though, it helps to understand these tools and their value.
Logs and integrated logging record the details of every action and activity within an application or microservice. These logs help track what is happening within your systems in a raw form.
Activity logs provide details when investigating a security issue or vulnerability. But using just logs to recreate security incidents or issues is challenging. Implementing a log collector and analyzer alongside our log collector allows us to filter and interpret raw logs instead of sifting through them independently.
Metrics record resource capacity and use as your applications and microservices consume them. These metrics can include infrastructure resource use, like memory or CPU, and other statistics, such as the time actions take or the number of times events occur.
Metrics are most useful for security and observability when we track them over a long period. This information can then show use trends and patterns that help us better pinpoint anomalies.
Distributed tracing builds on standard logging. It allows us to follow requests traveling through applications and microservices. By following different requests through our systems, we can better understand why issues are happening. We can also use these traces to see where requests start, if they go through security checks, if they call components we expect, and if all parts are performing as intended.
Visualizations help us identify normal and abnormal patterns in a system’s behavior. Seeing information in this way is more straightforward than trawling through large amounts of data. These patterns can help us quickly determine security vulnerabilities, cyberattacks, and bugs within our applications and systems.
AI and Machine Learning
AI and machine learning take the pattern concept further by building models that watch for patterns on our behalf. This pattern detection enables us to respond to emerging patterns quicker and catch anomalies and cybersecurity incidents before they take hold.
Finding and Patching Vulnerabilities
All of the previous components sound great for observing our systems or microservices, but how do we use them together to find and patch potential security issues? Let’s now examine five common security risks and discuss how this increased observability helps mitigate them.
Broken authentication mechanisms within your application or service can be an attractive target for attackers. Distributed tracing, along with logging, helps track down authentication issues.
Proactively, you can run through different scenarios within your application with distributed tracing enabled, stepping through to ensure that authentication components work as intended. Reactively, you can watch as users navigate through your application’s features, using detailed logging to ensure this navigation does not bypass authentication components.
Access Control Vulnerabilities
Similar to authentication vulnerabilities, robust access control mechanisms are essential for reducing access control vulnerabilities. You can ensure authenticated users only have access to the components that they have permission to use.
Using distributed tracing, we can step through and ensure that our application consistently and completely applies access controls to components after authentication. Additionally, detailed logging can provide more in-depth information about why users are not accessing components as they navigate through our applications.
Brute Force Attacks
Brute force attacks are the easiest for attackers to employ. Apart from gaining access to user accounts, these attacks can also cause other performance-related issues in our applications and systems.
The most superficial brute force attacks keep trying different username and password combinations as fast as possible to try and gain access using valid accounts. The more sophisticated attacks spread this traffic over more extended periods and with different source locations.
For the first attack style, performance metrics can give us quite a bit of information when our systems become more heavily accessed. Metrics such as invalid login attempts, system load, and traffic patterns can pinpoint attack attempts quickly.
More sophisticated attempts may not be as apparent with metrics over a short period. By layering visualizations on top, we can start to understand if attacks occur over a more extended period, such as increasing invalid login attempts over days or consistent attempts or load during specific times.
External Dependency Vulnerabilities
While we can test and fix our code within our systems, sometimes external dependencies can be attack vectors for bad actors. These vulnerabilities can be because nobody has updated the external dependency’s version in a long time or because external systems or code are compromised.
Visualizing all the components and services within your application and how requests flow through them goes a long way in ensuring our teams track and maintain systems dependencies as required. Using distributed tracing and visualizations to pinpoint where requests begin and how they travel through our systems helps trace and stop inappropriate requests in compromised systems.
Finally, another way cyberattacks succeed is through compromised user accounts. Hackers gain access to these accounts either through easily guessed passwords or social engineering.
Detecting when accounts are compromised can be highly challenging as the user is known and uses the system by design. However, AI and machine learning tools can help recognize patterns and also detect anomalies in these patterns.
When accounts are compromised, the user’s activity typically changes. This includes things like accessing or attempting to access different parts of the system or accessing the system from a different location. We can train AI models to flag temporarily compromised accounts and even have our systems suspend users until we verify their activity.
Building observability into systems, especially microservice-based applications, helps you understand how your application is working and how users interact with it. From a security perspective, these levels of observability can provide proactive security testing and reactive security investigations to understand what has happened within your application.
Observability can help you detect authentication vulnerabilities, access control vulnerabilities, brute force attacks, external dependency vulnerabilities, and compromised accounts. You can then patch these vulnerabilities or take other action to protect your systems before hackers find their way in.
To increase your application’s observability in a straightforward, practical manner, and improve your applications’ security and efficiency, check out the New Relic One platform.
If you’re interested in developing expert technical content that performs, let’s have a conversation today.