Check Point® Software Technologies Ltd.(link is external) announced the launch of its next generation Quantum(link is external) Smart-1 Management Appliances, delivering 2X increase in managed gateways and up to 70% higher log rate, with AI-powered security tools designed to meet the demands of hybrid enterprises.
Nobody said transitioning to a more dynamic and continuous process would be easy. However, failure, fear and skepticism should not give people license to remain stuck in their legacy systems or rush headlong into change. Let's examine what this term "bimodal IT" actually means, why it makes sense in some cases and how to ease the pain of transition.
Start with Getting Bimodal IT Right: Challenges and Choices - Part 1
Managing the Transition
The reason Gartner brought the idea of bimodal IT to light was to create breathing space so that organizations could transform and innovate without crashing and burning. The reason that Agile was created, for instance, was to enable a faster, more responsive process than waterfall practices can offer. However, switching to continuous delivery and integration mode too quickly could prove disastrous for certain systems, as some change carries more inherent risk than other changes. Following are key elements to consider when transitioning to ensure that applications continue to run at optimal levels.
Whichever mode apps are running in, they are doing so on complex and dynamic infrastructures more than ever, with underlying resources constantly changing to meet these applications' performance requirements. You need visibility into all your data — including performance data, logs and topology — and the ability to visualize all layers of your application infrastructure stack in one place at any point of time. This allows you to identify the root cause of an outage or performance degradation in the past or the present. These tools can also provide the capability to understand the impact of a software release on the operations in the Continuous Delivery and Integration mode (Mode 2). In the absence of such tools, conducting definitive post-mortem analysis is a costly, manual and confusing process — if it can be done at all.
IT is becoming increasingly complex and dynamic as it undergoes this transformation. There is a big data problem brewing in IT. Relying solely on traditional IT monitoring tools that trigger numerous alarms makes the job of IT operations teams even more difficult. Understanding all the raw data to make intelligent decisions in real time and sifting through the sea of alarms and telemetry data at the same time poses major challenge to IT operations teams. AI — especially machine learning — is well suited to take all the data and generate the necessary operational intelligence to distinguish critical, service-impacting events from false positives that do not require the immediate attention of an operator. As IT transitions, you need IT operations intelligence that can handle both modes of operations.
Predicting issues before they become problems is the key to preventing outages. A companion problem to the one above is that traditional monitoring tools trigger alerts only after a problem has already occurred. Look for solutions that incorporate predictive analytics to alert you to anomalous trends or potentially dangerous issues before they impact your application.
To ease and manage this transition, automated solutions that analyze and provide insight into ever-changing applications and infrastructure topologies are essential. Equipping users with the ability to replay and analyze past incidents and to pinpoint performance degradation and root cause, while cutting out the noise and preventing future costly outages and downtime, is important to facilitate the transition. This operational intelligence connects enterprise DevOps and TechOps teams, giving them what they need to quickly address issues as they arise.
Eyes on the Prize
There's no one solution that will work with every organization when it comes to digital transformation. IT Operations teams must take a close and hard look at which aspects can proceed to Mode 2 and which need to remain in Mode 1 for the time being. Transition times will vary, and some organizations will arrive fully at Mode 2 faster than others, but that's fine – it's not a race. Bimodal IT was never intended to be a permanent fix but a step in the right direction toward agile and dynamic IT. By providing visibility into systems and activities while keeping the business functioning without disruption, IT operations analytics can play a significant role in a successful, efficient transition.
Akhil Sahai, Ph.D., is VP Product Management at Perspica.
Industry News
Salesforce and Informatica have entered into an agreement for Salesforce to acquire Informatica.
Red Hat and Google Cloud announced an expanded collaboration to advance AI for enterprise applications by uniting Red Hat’s open source technologies with Google Cloud’s purpose-built infrastructure and Google’s family of open models, Gemma.
Mirantis announced Mirantis k0rdent Enterprise and Mirantis k0rdent Virtualization, unifying infrastructure for AI, containerized, and VM-based workloads through a Kubernetes-native model, streamlining operations for high-performance AI pipelines, modern microservices, and legacy applications alike.
Snyk launched the Snyk AI Trust Platform, an AI-native agentic platform specifically built to secure and govern software development in the AI Era.
Bit Cloud announced the general availability of Hope AI, its new AI-powered development agent that enables professional developers and organizations to build, share, deploy, and maintain complex applications using natural language prompts, specifications and design files.
AI-fueled attacks and hyperconnected IT environments have made threat exposure one of the most urgent cybersecurity challenges facing enterprises today. In response, Check Point® Software Technologies Ltd.(link is external) announced a definitive agreement to acquire Veriti Cybersecurity, the first fully automated, multi-vendor pre-emptive threat exposure and mitigation platform.
LambdaTest announced the launch of its Automation MCP Server, a solution designed to simplify and accelerate the process of triaging test failures.
DefectDojo announced the launch of their next-gen Security Operations Center (SOC) capabilities for DefectDojo Pro, which provides both SOC and AppSec professionals a unified platform for noise reduction and prioritization of SOC alerts and AppSec findings.
Check Point® Software Technologies Ltd.(link is external) has been recognized on Newsweek’s 2025 list of America’s Best Cybersecurity Companies(link is external).
Red Hat announced enhanced features to manage Red Hat Enterprise Linux.
StackHawk has taken on $12 Million in additional funding from Sapphire and Costanoa Ventures to help security teams keep up with the pace of AI-driven development.
Red Hat announced jointly-engineered, integrated and supported images for Red Hat Enterprise Linux across Amazon Web Services (AWS), Google Cloud and Microsoft Azure.
Komodor announced the integration of the Komodor platform with Internal Developer Portals (IDPs), starting with built-in support for Backstage and Port.
Operant AI announced Woodpecker, an open-source, automated red teaming engine, that will make advanced security testing accessible to organizations of all sizes.