Today’s world is becoming more and more digital and the COVID-19 pandemic has only accelerated this trend. To meet the growing demand for digital innovation, more and more companies are turning to cloud-native application architectures, based on microservices, containers and platforms like Kubernetes, for the agility they need. they offer. They’re also taking more agile approaches to software delivery, like DevSecOps.
However, the benefits of these approaches are not without risks. The dynamic nature of container-based native cloud environments and the need to keep up with the speed of agile development make it much more difficult to detect and manage application security vulnerabilities, with alerts and false positives besieging teams. So how can organizations adopt these approaches and ensure that their applications remain secure?
Architecture that never sleeps
In cloud native environments, change is the only constant. More than half (61%) of organizations say their environment changes once per minute or less, and almost a third say it changes at least once per second. Indeed, microservices are constantly bursting in and out of life, with an infrastructure being put in place to support them in real time, as customers and employees interact with digital services.
While this makes application execution more efficient, it also poses a challenge for security teams. The ever-changing nature of dynamic architectures means that businesses are exposed to thousands of vulnerabilities that continue to proliferate every minute. Additionally, the dynamic nature of today’s environments means that the dependencies of vulnerable components are also constantly changing, making it impossible to manually assess the impact.
This can have serious consequences. The 2017 Equifax vulnerability, in which the personal data of millions of people was stolen, occurred because hackers were able to exploit a widely known vulnerability in the Apache Struts library. This vulnerability still exists in countless web applications globally, but the increasing shift to cloud native architectures will make it increasingly difficult to identify.
Modernize security approaches
The problem is, traditional approaches to vulnerability management only offer a static view at any given time, making them prone to blind spots in dynamic environments. Not only do scanners often miss what is running in production, but they also create copious amounts of alerts and false positives on any possible vulnerabilities they detect, failing to be able to tell the difference between a potential vulnerability and a potential vulnerability. exposure. The large number of alerts prevent organizations from assessing their level of exposure, making the overall risk uncertain.
To overcome this, businesses need a new approach to application security that enables them to spot exposures as they arise. To do this, organizations must establish continuous observability in their cloud environment to eliminate blind spots. Combining this with AI can provide precise answers about the source, nature and severity of the vulnerabilities detected, as close to real time as possible. This intelligence can be used to power Runtime Application Self-Protection (RASP) capabilities, helping organizations to automatically detect, assess and manage application vulnerabilities in real time.
Additional pressure on developers
In addition to dynamic application architectures, agile approaches to software delivery and orchestration also create challenges. As innovation accelerates, DevSecOps approaches are also shifting the “left” responsibility to developers to ensure their code does not expose any exploitable vulnerabilities. Developers who are short on time simply don’t have the time to manually scan for vulnerabilities and are often inundated with alerts, many of which are low priority or false positives. Without the context of the impact each vulnerability has on the cloud native ecosystem as a whole and the applications and data it puts at risk, it is difficult for DevSecOps to prioritize actions to accelerate and secure release cycles. . As such, and often despite multiple security tools, even the most common vulnerabilities may go undetected by hackers.
Observability, artificial intelligence and automation also provide a solution to this dilemma, allowing DevSecOps teams to continuously analyze their entire cloud native environment – including applications, libraries and code – for identify any changes, prioritize alerts and eliminate false positives. With this AI assistance, developers can understand the source and nature of application security vulnerabilities, as well as their severity, allowing them to manage their time much more efficiently. This approach also allows DevSecOps teams to identify post-deployment attack vectors, allowing them to strengthen the defenses of applications running in production to protect against new vulnerabilities as they emerge.
Securing the cloud-native future
The key to securing cloud native applications is to ensure that the approaches used to secure them are just as dynamic as the environment in which they are deployed and the methodologies with which they are built. Application architectures and software delivery will only advance faster as we continue on the digital path. Instead of catching up in the future, organizations should be laying the groundwork now by adopting an application security strategy that can keep pace with transforming architectures and development methods. This means combining the solutions and processes needed to modernize approaches to application security, effectively support DevSecOps teams, and get the most out of cloud native environments and agile delivery.