Observability: Key to AI-Driven Automatic Protection
In the digital era, observability has become the ideal ally for IT management and cloud security. The reason: it ensures the protection of production and pre-production environments.
As infrastructures rapidly evolve, the cloud microservices market is experiencing significant growth. Reports from Mordor Intelligence estimate it will reach a value of $1.63 billion in 2024 and $4.57 billion in 2029, with a compound annual growth rate of 22.88% during this period.
To respond to these challenges, organizations require tools that can continuously monitor their applications and detect breaches in real-time.
According to a Gartner study, 99% of cloud security failures up to 2020 will be attributable to customer errors rather than technical failures by providers.
This underscores the importance of having observability solutions that provide a comprehensive view of the system and proactively respond to changes in IT environments.
In this article, we will explore how observability, driven by artificial intelligence (AI), offers an automated and proactive solution to protect cloud systems and mitigate security risks, ensuring that applications can operate safely and efficiently. Let’s dive in!
What is Observability and Why is it Crucial?
It is an approach that is especially useful in complex and rapidly evolving infrastructures, such as container-based or microservices architectures, where systems are constantly changing. Here’s why:
| Observability | The ability to understand the internal state of a system from the data it generates, allowing for a deeper and more detailed view of the processes running within the IT infrastructure. |
| Purpose | It aims to provide a contextual and detailed view of the overall system behavior. |
| Difference from Traditional Monitoring | Traditional monitoring usually focuses on known metrics and alerting on predefined issues, while observability. |
| Achievements | It allows for real-time detection and diagnosis of unexpected or undesirable changes, whether they are performance issues, system failures, or security vulnerabilities. |
In fact, according to an IDC report, 90% of new applications developed by 2025 are expected to run on microservices-based architectures. This increases the need for advanced observability that enables IT teams to effectively manage these complex environments.
This type of observability is achieved through the combination of metrics, logs, and traces, providing complete visibility of every event in the system and allowing proactive action against incidents that would otherwise go unnoticed until they cause major problems.
Main Features of Observability
- Real-Time Detection: Proactively identifies changes in the application environment, such as container dynamics, elastic scaling, or multiple version deployments.
- AI-Driven Automation: Instantly analyzes danger alerts, prioritizing them.
- Continuous Protection: Even if security checks are not integrated into development pipelines, observability ensures that any present vulnerability is immediately detected.
Cloud Security Challenges and Common Vulnerabilities
With the growth of cloud services, cloud security has become a top priority. Some of the most common issues include:
- Container Dynamics: The ephemeral nature and rapid scaling of containers make it difficult to detect traditional security issues.
- Multiple Versions and Blue/Green Deployments: These deployment strategies introduce additional complexities that can hide danger zones if not properly managed.
- Runtime Updates and Rollbacks: Continuous changes in environments can create security gaps if not actively monitored.
Most Frequent Vulnerabilities in Cloud Environments:
| Issue | Description | Impact |
| Code Injection | Exploit that allows attackers to insert malicious code into an application | Total system compromise |
| Authentication Failures | Errors that allow unauthorized access | Data theft, illegal access |
| Vulnerable Libraries | Use of outdated or insecure software | Exposure to known exploits |
| Incorrect Cloud Configuration | Misconfigurations that expose data | Data loss or breaches |
AI-Driven Automatic Protection and Cyberattack Detection
One of the greatest advantages of using artificial intelligence (AI) in IT management systems is its ability to identify problems and respond automatically and in real-time.
Unlike traditional approaches, which often require manual intervention and post-mortem analysis of incidents, AI can:
- Analyze large volumes of data in seconds.
- Detect anomalous patterns and emerging threats.
- React before they affect the operation of applications or infrastructure.
This instant reaction capability is crucial in dynamic environments like the cloud, where changes are constant and attacks can appear at any time.
Runtime introspection, which refers to the ability to analyze and monitor the behavior of applications while they are running, is key to this process.
According to McKinsey, organizations that adopt AI-based solutions for cybersecurity experience a 30% reduction in incident detection and response time.
How Does Automatic Vulnerability Detection Work?
AI can prioritize breaches based on their potential impact and the criticality of the affected system, making it easier for IT teams to make informed decisions and focus their efforts on the most severe threats.
- Continuous Monitoring.
- Runtime Detection.
- Automatic Response.
Key Benefits
- Reduced risk of security breaches.
- Alert prioritization.
- Time and resource savings.
Practical Cases of Observability Implementations
Some of the most common implementations, such as A/B testing and blue/green deployments, are essential to maintaining the integrity and security of applications, ensuring a controlled environment that allows identifying failures before they affect end users.
A/B Testing: Security and Performance Simultaneously
A/B testing is fundamental for optimizing applications, comparing two or more versions with a subset of users before implementing them globally.
However, this approach also introduces risks, as one variant could have danger zones not present in the other.
An example of this type of implementation can be seen in e-commerce platforms or services like Netflix, which use A/B testing to implement new features, ensuring that any running version is protected against vulnerabilities before a full deployment.
Blue/Green Deployments: Greater Control and Security at Every Stage
Blue/green deployments are software implementation strategies that allow teams to run one version (green) while keeping the previous one (blue) in operation.
This ensures a seamless transition but also introduces the challenge of protecting both versions from attacks.
This type of deployment is common in large cloud service platforms, such as AWS, which use this strategy to ensure secure and uninterrupted updates, maintaining the integrity of applications in production.
As you can see, observability, driven by AI, has transformed the way organizations protect their IT environments.
With its ability to detect vulnerabilities in real-time, prioritize alerts, and automate responses, it has become an indispensable tool in the fight against cyber threats.
Companies that adopt this technology can be confident that their cloud infrastructure will be proactively and efficiently protected, even in the face of the most dynamic and complex changes.
Interested in Automatically and Continuously Protecting Your Infrastructure?
Learn more about how observability and advanced cloud management services can transform the way your company manages the security and performance of its systems.
At Ikusi, we offer customized solutions specifically designed to ensure the security, performance, and stability of your cloud systems.