Modern app deployment demands unprecedented levels of portability, scalability, and reproducibility.
With the end user expectation of app services being up and running and always available on demand, monolithic applications can no longer deliver to such requirements, and are being rapidly displaced by container-based application services. The flexibility that robust containerization brings is underscored by the necessity to manage it efficiently, in an agile, secure, and resource-minded way. This is where Kubernetes, a container orchestration platform, excels, bringing about its rapid adoption in modern app deployments and with it, a heightened need for Kubernetes security.
As a highly flexible yet complex system, Kubernetes presents both unique opportunities and challenges for its operators, including specific security challenges that need to be taken into account together with more general cloud environment security principles and protocols.
In this article, we will provide an overview of the security basics of Kubernetes environments, the role a Security Operations Center (SOC) plays in securing them, and the potential impact and benefit of LLMs and AI on both emerging security threats and the opportunities for better cyber protection in enterprise environments running Kubernetes.
Understanding Kubernetes Security Challenges
The basic building blocks of Kubernetes architecture include pods, clusters, and nodes. Pods are the smallest deployable instance of a Kubernetes platform that each hosts one or several containers that work together as part of an application service. Pods are run within nodes, either physical or virtual, according to the predefined desired state that governs the overall running of a Kubernetes system. In turn, nodes are placed into a cluster, each governed by the control plane overseeing the cluster’s health, orchestrating its lifecycle and schedule, and maintaining the desired state. A Kubernetes setup may include one or multiple clusters, the latter being increasingly more common in enterprise environments.
The advantages of Kubernetes lie in the modularity it offers: while the desired state is predefined, its execution isn’t static, benefiting from the dynamic allocation of resources where they are needed the most. Pods can be moved, recreated, updated, and taken offline by the control plane to achieve the desired state. Clusters in a multi-cluster environment can be duplicated for resilience or contain separate environments for different DevOps functions like staging and production.
Still, the unique nature and dependencies introduced by Kubernetes architecture carry security risks, just like other cloud-native environments. Some of the most common risks include:
Misconfigurations of Kubernetes’s native role control feature, RBAC (role-based access control). Common pitfalls include failure to adjust default settings that often grant excessive permissions, duplicate user roles that make privilege revocation difficult, and introducing avenues for unintended privilege escalation.
Exposed dashboards and insecure APIs. Kubernetes relies on APIs for additional interoperability, but the access to APIs is carried through its command-line interface or HTTP requests to the API server, both of which may be targeted in cyber attacks if not properly secured.
Unrestricted network traffic between pods. Kubernetes pods can communicate freely with each other by default, so if containers in one are compromised, malicious access may be gained to others in the cluster, especially if RBAC is misconfigured as well.
Vulnerabilities in container images. Many architectures include container images that are insufficiently secured and run outdated software or unnecessary tools, putting the cluster at risk if proper image scanning policies aren’t in place.
Threat hunting in the Security Operations Center (SOC)
In an enterprise setting where the security of data, transactions, and operations is not only paramount but begets complexity that involves dozens of teams, moving parts, and protocols, establishing a SOC, or Security Operations Center, goes a long way towards assuring the cyber and IT security in an organization is not an afterthought, but a meticulous, planned, controlled matter that’s imbued in the functioning of all environments.
When Kubernetes is a part of an enterprise technological landscape, highly skilled SOC operators are instrumental in monitoring its health and security, given how attractive a target a Kubernetes setup becomes for attackers and malicious actors. Engaging in proactive threat hunting where cloud environments, including Kubernetes, are concerned becomes essential for security processes.
The key functions of a SOC that play into the increased security and the maintenance of the integrity of Kubernetes environments include:
Real-time detection of anomalies in user behavior, unexpected changes in RBAC, unsanctioned or unjustified privilege escalation, or lateral movements that may indicate an impending or emerging attack
Correlating events across containerized workloads for analysis and detection of patterns to surface potential problems, detect security gaps, and improve incident response
Defining and enforcing security policy and compliance controls based on the industry best practices and prescriptive recommendations of leading security institutions such as NIST or CIS Benchmarks
Proposing and overseeing the integration of Kubernetes with Security Information and Event Management (SIEM) tools and container-specific security tools like Falco, Sysdig, and Aqua to improve runtime and gain real-time visibility into the app behavior and processes.
Complexity as one of the defining traits of Kubernetes environments necessitates that automation, machine learning, AI, and LLMs play a bigger role in maintaining its integrity in an enterprise setting with many intertwined systems and environments being monitored and safeguarded at once.
LLMs and AI in Kubernetes Security
The rapid advancement in the capabilities of large language models (LLM) brought about the ubiquity of AI and machine learning tools, and unsurprisingly so: LLMs in the context of security environments is a force multiplier for security teams, able to lend analytical and data-collating support at scale across multiple environments while still depending on highly skilled human operators to make decisions and guide the AI’s hand to execute and automate.
It is not just the technological prowess of LLMs that is useful in security environments, but their ability to receive and understand commands, be queried, and explain various operational aspects in plain language, and generate templates, documents, and policies based on human input that is so valuable. Some use cases and examples of the above include:
Natural language threat queries across logs. For example, “Show me suspicious activity in cluster A”
Explaining alerts in plain language for faster triage and improved response time
Generating security policy templates like PodSecurityPolicy, Kyverno, and OPA
The ability to respond to questions from human users about Kubernetes deployment and propose action items to address issues
Improving Kubernetes observability by scanning the Kubernetes environments for potential issues and managing the associated data to facilitate its analysis by human teams.
It is important to remember that LLMs can be a valuable assistant, but the weight of executive decisions should still rest on the humans using them. The human oversight that governs the use of AI in the security of environments remains key to the successful integration of automation into security operations. Using a Kubernetes analogy, the desired state is always dictated by humans, who then rely on the LLMs and smart technology to deliver on the rest.
Conclusion
As the deployment, usage, and popularity of Kubernetes grow, so do the security risks and the volumes of cyber attacks targeting them. When an organization makes a decision to implement Kubernetes, a step both complex and costly at the start, the associated security and the strength of its security posture must scale with the growing Kubernetes environments.
The SOC, in tandem with the growing capacity of LLMs to assist, offers powerful ways to gain visibility into the ongoing operations of Kubernetes and stay ahead of existing and emerging cybersecurity threats. Now is the best time to review your Kubernetes cluster security posture and explore how AI tools can further support your team.
