Making microsegmentation a table stakes requirement for gaining the speed, scale, and time-to-market advantages that multicloud tech stacks deliver their new digital-first business efforts is critical for securing future growth.
Gartner expects that through 2023, at least 99% of cloud security breaches will be the user’s fault. Getting microsegmentation correctly in multicloud deployments can make or break any zero-trust project. Ninety percent of organizations transitioning to the cloud use zero trust, yet just 22% are convinced their organization will capitalize on its many benefits and improve their business. According to Zscaler’s The State of Zero Trust Transformation 2023 Report, secure cloud transformation is difficult with conventional network security infrastructure such as firewalls and VPNs.
Defining microsegmentation
In order to reduce the lateral blast radius in the event of a breach, microsegmentation divides network environments into smaller parts and applies granular security measures. In order to reduce the number of attack surfaces and restrict lateral movement, network microsegmentation tries to separate and isolate specific portions in a business network.
The zero-trust framework developed by NIST defines it as one of the key elements of zero trust. In large-scale, complicated multicloud and hybrid cloud infrastructure configurations, microsegmentation is a problem, according to CISOs, who see the potential for AI and machine learning (ML) to dramatically improve their deployment and use.
According to Gartner, microsegmentation is the “capability to insert a security policy into the access layer between any two workloads in the same extended data centre.” Microsegmentation technologies make it possible to define fine-grained network zones that include certain assets and applications.
Zero trust relies on microsegmentation
The more hybrid and multicloud the environment, the more critical and difficult microsegmentation becomes, CISOs told. Many CISOs plan to use microsegmentation in the latter stages of their zero-trust projects, after they’ve picked up a few fast victories.
If you don’t perform the micro-segmentation, David Holmes, a senior analyst at Forrester, stated during the webinar that you won’t actually be able to convincingly claim that you undertook a zero trust journey. The time has come for microsegmentation.
The worldwide 2000 will always have a physical network, the person he was speaking to recently indicated, according to Holmes. They probably have it right, he said. The necessity to microsegment that will arise eventually.
The CIOs and CISOs who have successfully implemented microsegmentation encourage their peers to build their network security architectures with zero trust first, focusing on securing identities that are frequently under attack, along with apps and data, rather than the network perimeter. By 2026, 60% of companies pursuing zero trust architecture, up from less than 5% in 2023, will employ more than one microsegmentation deployment method, according to Gartner.
Every top provider of microsegmentation is actively engaged in R&D, DevOps, and prospective acquisition strategies to advance their knowledge of AI and ML. A few of the top vendors are Akamai, Airgap Networks, AlgoSec, Amazon Web Services, Cisco, ColorTokens, Elisity, Fortinet, Google, Illumio, Microsoft Azure, Onclave Networks, Palo Alto Networks, Tempered Networks, TrueFort, Tufin, VMware, Zero Networks, and Zscaler.
Network-based, hypervisor-based, and host-agent-based types of solutions are only a few of the solutions that microsegmentation companies provide.
How AI and ML can strengthen and simplify microsegmentation
A perfect application for AI, ML, and the developing field of new generative AI apps based on private Large Language Models (LLMs) is improving the accuracy, speed, and scale of microsegmentation. Microsegmentation is frequently planned at the end of a zero trust framework’s roadmap because large-scale implementation is frequently more time-consuming than anticipated.
By automating the most labor-intensive parts of implementation, AI and ML can assist raise the chances of success earlier in a zero-trust endeavour. By requiring the least privileged access for every resource and protecting every identity, outcomes are strengthened even further when ML algorithms are used to learn how an implementation might be optimized.
On-premise private networks are one of the most difficult domains to secure, according to Forrester, which found that the majority of microsegmentation initiatives fail. The private networks of the majority of organizations are also flat and resist the kind of microsegmentation required for complete infrastructure security. The flatter the private network, the harder it is to limit the impact of malware, ransomware, open-source assaults like Log4j, unauthorized use of privileged access credentials, and other cyberattacks.
Startups entering the market
The numerous difficulties that microsegmentation poses create a business opportunity for startups. Startups with unique strategies for addressing the issues of microsegmentation faced by businesses include Airgap Networks, AppGate SDP, Avocado Systems, and Byos. One of the top twenty startups with zero trust to keep an eye on in 2023 is AirGap Networks. The attack surface of each linked endpoint on a network is reduced by their method of agentless microsegmentation. It is feasible to segment every endpoint inside an organization while integrating the solution into an active network without requiring device changes, downtime, or hardware updates.
Graph databases and GPT-3 models are used by Airgap Networks’ Zero Trust Firewall (ZTFW) with ThreatGPT to assist SecOps teams in learning new threat information. While graph databases offer contextual insight on endpoint traffic relationships, GPT-3 models analzse natural language queries to pinpoint security vulnerabilities.
Prime areas for AI and ML
The following areas benefit greatly from the accuracy, speed, and scale that AI and ML can provide in microsegmentation:
Manually setting and managing access controls between workloads is one of the most challenging components of microsegmentation. Automatic modelling of application dependencies, communication flows, and security regulations is possible with AI and ML algorithms. IT and SecOps teams may spend less time on policy administration by utilizing AI and ML to address these issues. The capacity of AI in microsegmentation to model suggested policy changes and spot potential hiccups before imposing them is another perfect use case.
More insightful, real-time analytics
Utilizing the various real-time telemetry sources to create a consistent reporting strategy that offers deep visibility into network settings is another difficulty in deploying microsegmentation. A thorough understanding of communication and process flows between workloads is provided by real-time analytics methods based on AI and ML. Anomalies and risks in east-west traffic patterns have been successfully identified using advanced behavioral analytics offered by ML-based algorithms. By streamlining management, these analytics enhance security.
More autonomous asset segmentation and discovery
Without human assistance, AI can spread segmentation policies, build communication channels, find anomalies, and identify assets. This self-sufficient capability reduces the effort and time required to carry out microsegmentation and upholds its currency as assets change. It also lessens the chance of human error in policy formation.
Large-scale anomaly detection
Massive volumes of network traffic data can be analyzed by AI algorithms, allowing for the detection of unusual patterns. This enhances security measures and maintains optimal speed. Utilising AI for anomaly detection allows microsegmentation to spread across large hybrid settings without significantly increasing overhead or latency. This guarantees the maintenance of security efficacy despite the environment’s growth.
Streamlining integration with cloud and hybrid environments
By spotting obstacles in the way of achieving optimized scalability and policy enforcement, AI can enhance the integration of microsegmentation across on-premises, public cloud, and hybrid systems. Integrating with AI makes heterogeneous environments more secure by removing any gaps that an attacker could use to their advantage. Additionally, it lessens operational complexity.
Automating incident response
Response times are shortened by automated reactions to security incidents made possible by AI. Anomalies and malicious behavior patterns in network traffic and workflow can be quickly and accurately identified by microsegmentation solutions using trained ML models. Large datasets of typical traffic patterns and well-known attack fingerprints can be used to train these models to find new threats. When a model identifies a possible problem, prepared playbooks can launch automated response activities, like quarantining concerned workloads, preventing lateral movement, and notifying security teams.
Improved collaboration and automation of the workflow
AI accelerates teamwork and automates processes, cutting down on the amount of time needed for planning, analysis, and implementation. The entire microsegmentation lifecycle has been optimized using AI, allowing for a shorter time-to-value and continued adaptability, increasing the productivity of security teams.
Essential to zero trust architecture
Zero trust architecture requires microsegmentation, but scaling it is challenging. In a number of crucial areas, such as automating policy management, offering real-time insights, enabling autonomous discovery and segmentation, and more, AI and ML have the ability to strengthen and streamline microsegmentation.
AI and ML can assist organizations find the obstacles impeding their progress on microsegmentation projects and figure out how to overcome them more quickly. Accuracy, speed, and scalability of AI and ML enable businesses to overcome implementation difficulties and enhance microsegmentation. Businesses may grow securely in complicated multicloud environments while reducing the blast radius, halting lateral movement, and stopping unauthorized access.
