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Enterprise Management Associates’ (EMA) Network Management Megatrends research has been benchmarking enterprise network operations strategies for 18 years. It is the definitive examination of how IT organizations manage their networks while adapting to major industry disruptions such as cloud transformation and AI.This year’s Megatrends report, based on a survey of 352 IT professionals, revealed only 31% of enterprises are completely successful with their network operations strategy. Network teams are struggling to prevent network outages, overwhelmed by alert noise, and struggling with tool sprawl.Join EMA Vice President of Research Shamus McGillicuddy for a webinar that will highlight the following findings from the 2026 Network Management Megatrends research:

The ways that network operations teams are trying to improve their network observability toolsets
Emerging approaches to automating more of day-to-day operations
The current state of managing hybrid, multi-cloud networks
How IT organizations are retooling network observability to manage AI workloads and traffic on their networks

RSAC 2026 highlighted a growing disconnect between cybersecurity innovation and business enablement. Amid the surge of “AI-powered” solutions, many organizations risk repeating a familiar mistake: prioritizing compliance, complexity, and hype over real-world outcomes.This blog from EMA's Ken Buckler explores how security measures—past and present—can unintentionally hinder productivity, leading teams to bypass controls in order to get work done. Drawing a parallel between a simple “pen cap hack” and today’s influx of AI-driven tools, it underscores the dangers of security theater and misaligned priorities. The takeaway is clear: effective cybersecurity should reduce friction, support business velocity, and deliver measurable risk reduction—not add cost and complexity without value.

If you thought "Q-Day" was a problem for the 2040s, it’s time to check your calendar: the goalposts just moved (again), and they moved significantly closer (again).We’ve talked about Post-Quantum Cryptography (PQC) for a while now, in our regular podcast and most recently with the announcement from Google. But Cloudflare’s recent announcement—targeting full post-quantum security, including authentication, by 2029—changes the vibe entirely. When one of the biggest gatekeepers of the internet sets a hard deadline three years out from now, the rest of us need to stop treating this like a science project and start treating it like a migration project.

The enterprise networking landscape is currently undergoing its most radical architectural transformation since the inception of the internet, driven by the obsolescence of the traditional division between connectivity and security. Historically, networking teams managed bandwidth "plumbing" while security teams maintained perimeter defenses. However, the dual pressures of cloud ubiquity and a distributed workforce have rendered this bifurcated model ineffective, forcing a convergence of these formerly distinct disciplines.As organizations transition from rigid private circuits to agile Software-Defined Wide Area Networking (SD-WAN), they have gained cost-efficiency but inadvertently expanded their attack surface by pushing traffic onto the public internet. This tension has catalyzed the rise of Secure Access Service Edge (SASE). SASE represents a strategic philosophy that replaces location-based security with an identity-centric infrastructure, weaving protection directly into the network fabric to resolve the long-standing paradox between performance and protection. By moving rigorous security inspection to the cloud edge, enterprises can minimize latency while maximizing defense.This evolution is being further accelerated by the integration of Artificial Intelligence and Machine Learning. As modern network environments reach levels of complexity that defy manual human intervention, the industry is pivoting toward "intent-based" systems. These advanced architectures are designed to be self-healing and self-optimizing, capable of autonomously adjusting to performance fluctuations or emerging security threats in real time. Ultimately, this shift marks the end of the standalone network, replaced by a unified, intelligent service delivery model.Join EMA’s Chris Steffen and experts from Coevolve, Versa Networks, and Zscaler to explore new WAN transformation research findings and the trends reshaping enterprise networking.

ThoughtSpot has announced Spotter Semantics, an agentic semantic layer designed to provide governed business context to AI agents at enterprise scale. The release extends ThoughtSpot’s existing search-token architecture with next-generation search tokens and aggregate awareness, a centralized metrics catalog, and a Model Context Protocol (MCP) server that connects the semantic layer to external AI agents and LLMs. ThoughtSpot positions Spotter Semantics as a deterministic alternative to text-to-SQL approaches, using its patented search-token engine rather than LLM-generated SQL to translate natural language into governed queries.

Alation has announced general availability of Alation Curation Automation alongside a broader framework that the company calls outcome-based governance. The core idea is that instead of assigning governance tasks and hoping teams follow through, organizations define the outcome they need and the platform enforces and maintains those standards through purpose-built agents and continuous automation. Curation Automation is the final piece, handling metadata enrichment at scale and joining Alation’s existing CDE Manager and Data Quality products to complete the system.

Confluent has announced new capabilities within Confluent Intelligence, its fully managed service for building real-time AI systems on Confluent Cloud. The release introduces two additions: Streaming Agents, which use the Agent2Agent (A2A) protocol to connect and coordinate AI agents across enterprise systems using real-time data streams, and Multivariate Anomaly Detection, a built-in ML function that analyzes multiple metrics jointly to detect complex anomalies with fewer false positives.

At its annual Domopalooza conference, Domo announced a broad set of capabilities that together signal a strategic repositioning from BI platform to AI orchestration layer. New agentic AI capabilities include AI Agent Builder, AI Toolkits, a centralized AI Library, and a Domo MCP Server that connects enterprise data and workflows to external AI platforms through the Model Context Protocol standard. BI modernization capabilities include Worksheets (a spreadsheet-native exploration interface, now GA), Report Builder for PDF (beta), Data Models for reusable dataset relationships (beta), and semantic layer enhancements that formalize business definitions across the platform.

Welcome to the Enterprise Management Associates™ (EMA) PRISM report on post-quantum cryptography (PQC) solutions. EMA is an industry-leading analyst firm specializing in a wide range of technology areas, including cybersecurity. We are dedicated to providing comprehensive research, analysis, and insights to help organizations make informed decisions about their technology investments.The EMA PRISM report is a broad overview of a particular product set in a larger technology space. It is designed to provide practitioners and business leaders with a starting point for solutions in a common vertical. It uses publicly available information and sentiments, as well as the expertise of EMA researchers, to create an easy-to-understand and digestible overview of significant vendors/solutions in a space. It is NOT meant to be a detailed or thorough examination of the solutions in that vertical, nor does it include all of the solutions in that vertical.The information presented in this report acts as a starting point for decision-makers and practitioners to evaluate the vendors and solutions that best align with your organization’s needs and requirements.

As AI transforms the enterprise, the financial sector, including banking, financial services, and insurance (BFSI), is deliberately slowing deployments to As AI transforms the enterprise, the financial sector, including banking, financial services, and insurance (BFSI), is deliberately slowing deployments to prioritize risk mitigation, resulting in lower adoption rates.Yet even as firms express confidence in their ability to scale AI, new survey data from Enterprise Management Associates (EMA) reveals a critical roadblock: legacy Identity and Access Management (IAM) systems are not keeping pace. Compared to other sectors, financial institutions report significant shortfalls in key areas such as resiliency (11.8%) and security (10.8%)—gaps that directly impact their ability to safely implement AI.This operational disconnect has led to what can be described as a “SaaS paradox.” Financial organizations are 17.2% more likely to prefer all-in-one platforms, yet they still require deep customization and large scale to function.Join Ken Buckler, Research Director at EMA, and Damon Tepe, Head of Product Marketing at Ory, for an insightful webinar as they:

Unpack these new survey findings and compare financial services data with broader industry trends
Examine where traditional IAM platforms fall short in supporting emerging agentic AI use cases
Explore how a composable IAM approach can help organizations innovate without compromising compliance

In this blog, EMA’s Herb Blecher shares a hands-on journey from ChatGPT to OpenClaw to reveal a bigger issue: the “agentic enterprise” isn’t a tool problem, it’s an architecture problem.

While AI tools excel at reasoning with the right context, they still struggle with execution, especially across systems. Add in challenges around observability, scalability, and cost, and it’s clear most organizations aren’t ready to move from pilot to production.

The takeaway? Success with agentic AI isn’t about picking the right tool, it’s about building the infrastructure to make them work together.

AI isn’t coming for your job, but it is changing it.In this new blog, EMA’s Chris Steffen explains that just like virtualization, cloud, and DevOps before it, AI is a force multiplier not a replacement. It’s taking over repetitive tasks and freeing IT professionals to focus on higher-value work.What AI can’t replicate? Experience, intuition, and the “tribal knowledge” built over years in the field. That human expertise is still critical, especially as overreliance on AI can introduce risk without proper oversight.

Apica has expanded its Ascent product suite with Ascent 2.16, positioning it as agentic-ready telemetry infrastructure built for AI-scale operations. The release emphasizes a pipeline-first architecture that processes, enriches, and governs telemetry before it reaches downstream observability platforms.

Agentic AI is pushing observability beyond insight into autonomous action.

In this episode of the Enterprise Automation Excellence podcast, Dan Twing and Tom O’Rourke sit down with Arturo Oliver, Senior Director, Market Strategy and Analyst Relations at ScienceLogic, to explore how observability is evolving from reactive diagnosis to proactive prevention powered by AI agents.

Key shifts discussed:

From root-cause analysis to prediction and prevention
From noise reduction to AI-driven recommendations
From scripted automation to autonomous, action-taking agents

But the real challenge ahead? Connecting agents across tools and systems and building the trust, transparency, and institutional knowledge required to make agentic operations work at scale.

Key takeaway for automation leaders: Don’t wait for perfection. Start finding practical agentic AI wins now while prioritizing prevention, context, and verifiable AI outcomes.

Listen to the full episode to learn how observability is becoming the foundation for autonomous IT operations.

The timeline for "Q-Day"—the hypothetical day when quantum computers become powerful enough to break modern digital security—has shifted from a distant worry to an urgent reality. The latest research from Google Quantum AI and a startup called Oratomic has revealed that the "skeleton key" needed to crack current encryption is much closer than previously thought. This matters for everyone from Bitcoin holders to corporate leaders.

In conversations with network teams, one theme keeps emerging: AI-powered management tools are no longer a “nice to have.” Engineers want agentic interfaces that can gather data, detect issues, and help respond to problems across DNS, DHCP, and IP address management (DDI) environments.

EMA’s DDI Directions 2026 research shows:

43% say AI capabilities are essential when evaluating DDI vendors
Nearly all organizations would use AI features if available
66% want agentic AI that can proactively detect and respond to issues — not just chatbots

The challenge? Most DDI vendors are still early in their AI journey.

As agentic capabilities begin to arrive over the next 6–12 months, network teams will need to balance innovation with caution. Early adopters already report challenges validating AI insights — making governance and verification critical.

The message is clear: the future of core network services is agentic, but evaluation and trust will matter as much as automation.

DNS may be known as the “phone book of the internet,” but it’s quickly becoming one of the most exploited layers of enterprise infrastructure.

In this episode of the Mean Time to Insight podcast, EMA’s VP of Research Shamus McGillicuddy explores why DNS security is rising as a critical concern for network and security teams — from real-world misconfiguration risks to AI-enhanced attacks targeting DNS environments.

Key insights include:

40% of enterprises experienced a DDI-related security breach in the past two years
Malicious redirection and DNS abuse rank among top security concerns
Visibility, automation, and cross-team ownership are essential to improving DNS security posture

The takeaway: DNS isn’t just networking plumbing, it’s a frontline cybersecurity asset.

Listen to the full episode to learn how organizations can strengthen DNS security and reduce hidden risk.

Modern digital businesses must make critical decisions in real time, yet many teams remain reactive, overwhelmed by data that offers little insight into actual business impact. Traditional metrics and alerts fail to show what matters most, who is impacted, or how issues affect revenue-generating and mission-critical processes as they unfold.Recent Enterprise Management Associates (EMA) research highlights the urgency of this challenge, with many panelists suggesting the same improvement, best summarized by one individual response: “I would improve the end-to-end visibility across services, infrastructure, and applications. Right now, monitoring is fragmented across multiple tools, making it difficult to quickly identify root causes of incidents.”55% of the panelists in this same survey report that their teams are “moving to a cross-functional team approach” or that they’re expressly “organized to align with business services.” These realities mean that AI-powered observability across traditional silos of business and IT are now required. So, how do these organizations succeed?Join experts from EMA Research, Dynatrace, and DXC to learn how leading teams shift from monitoring system performance to understanding business outcomes.Additionally, you’ll discover how organizations:

Get real-time visibility into customer journeys and revenue-building transactions
Prioritize issues based on business impact—not just technical severity
Use AI and intelligent agents to anticipate problems and guide action
Align IT, operations, and business teams around shared, outcome-driven insights

Register today to gain practical strategies for aligning IT performance with real business results.

The cybersecurity world descended on San Francisco's Moscone Center this week for RSAC 2026, the 35th annual flagship event – and the numbers alone tell you this is no niche gathering. Around 43,500 attendees, 700 speakers, 600 exhibitors, and 400 members of the media converged on the conference, which spans multiple stages and countless hands-on experiences. That's a lot of people, and a lot of opinions; some of which are ones I hope you are paying attention to, and some that I would prefer you ignore. After spending the week meeting with some of the best vendors at the conference, as well as a few passing conversations in elevators and airplane queues, four themes kept coming up again and again – and if you're serious about your security program, they're worth paying attention to.

Actian, the data and AI division of HCLSoftware, has launched Data Observability Agents and a Model Context Protocol (MCP) server for Actian Data Observability. The release targets a growing enterprise risk: AI agents acting on data without verifying its quality at the point of execution.

AIOps platforms have improved visibility and reduced alert noise, yet many operations teams still rely on humans to interpret signals, triage incidents, and execute remediation. The next phase of operational intelligence introduces role-based AI agents designed to support specific operational responsibilities—from frontline responders to platform specialists.In this webinar, Enterprise Management Associates (EMA) Vice President of Research Shamus McGillicuddy will be joined by Grokstream’s leadership team. Josh Kindiger, Co-Founder & COO, will present the path to Predictive and Agentic Operations, outlining how classical machine learning, generative AI, and operational memory are driving self-healing IT and network operations. Larry Lien, Chief Product Officer, will provide a live demonstration of Grok’s L1 Agent, showcasing how role-based agents reason over signals, orchestrate workflows, reflect on outcomes, and safely execute actions at the frontline.The session will examine:

How today’s IT and network operations teams are embracing AI solutions
The challenges and pitfalls IT groups should watch for with AI
The architectural foundations of role-based agentic operations
How L1 operational agents are transforming frontline incident response

Databricks has launched Genie Code, an autonomous AI agent designed to handle data engineering, data science, and analytics workflows end-to-end. Databricks describes the agent as going beyond code assistance, capable of reasoning through problems, planning multi-step approaches, writing and validating production-grade code, and maintaining the result. Alongside the launch, Databricks announced the acquisition of Quotient AI, a platform specializing in evaluation and reinforcement learning for AI agents. Quotient’s capabilities will be embedded across Genie, Genie Code, and Agent Bricks to provide continuous performance monitoring, regression detection, and automated improvement cycles.

Monte Carlo has announced new Agent Observability capabilities that provide unified visibility across the full lifecycle of AI agents in production. The release is organized into four monitoring domains: context (validating the data agents rely on), performance (tracking cost, latency, and error rates), behavior (verifying agents follow intended workflows), and outputs (evaluating quality before and after deployment). New features include Agent Metric Monitors, Agent Trajectory Monitors for workflow validation, pre-production evaluations against golden datasets integrated into CI/CD pipelines, and a hosted OpenTelemetry deployment option in AWS. The release also expands platform support to Google BigQuery and AWS Athena.

Virtana has launched a new Application Observability capability that connects application performance issues to the system dependencies behind them, including infrastructure, networks, storage, Kubernetes, and AI workloads. Virtana frames the offering as a step beyond code-centric Application Performance Monitoring (APM), correlating signals across the enterprise stack and identifying root causes without forcing operators to stitch insights together across multiple tools.

ServiceNow launched Autonomous Workforce, a new capability on the ServiceNow AI Platform that deploys AI specialists to execute enterprise work from end to end with defined scope, authority, and governance. At the same time, ServiceNow introduced EmployeeWorks, which combines Moveworks’ conversational AI and enterprise search with ServiceNow’s unified portal and autonomous workflows. Together, these offerings connect natural language requests to governed execution across IT, HR, procurement, and other functions. ServiceNow positions Autonomous Workforce and EmployeeWorks as a platform solution to fragmented, feature-level AI by unifying intelligence, workflows, and control, so enterprises can convert AI from pilots and experiments into measurable outcomes at scale.

ControlUp recently acquired Unipath to accelerate its Autonomous Endpoint Management (AEM) strategy. By introducing agentic AI capabilities, ControlUp now automates remediation without requiring manual scripting or complex workflow design. This acquisition represents a major leap over traditional automation; it combines real-time telemetry with AI-led contextual decision-making to remove friction from issue detection, analysis, and resolution. By embedding Unipath’s self-learning automation into its Digital Employee Experience (DEX) platform, ControlUp empowers IT teams to shift toward autonomous operations, boosting employee productivity while slashing helpdesk demand and operational complexity.

Airrived announced the launch of AetherClaw, a governance and security execution layer designed to make agentic AI viable for enterprise deployment. Introduced at RSA Conference 2026, AetherClaw addresses a critical gap in early agentic frameworks: the absence of policy enforcement, auditability, and structured human oversight. The solution functions as a purpose-built control layer—delivering fine-grained access control, policy-as-code governance, and full audit trails embedded directly into agent execution.

Broadcom has released Automation Analytics & Intelligence (AAI) 24.4, strengthening its long-established automation analytics platform with expanded cross-scheduler correlation, predictive SLA modeling, and contextual workflow insight.

Snowflake announced a definitive agreement to acquire Observe, Inc., extending its AI Data Cloud strategy into the $50+ billion IT operations management market through large-scale observability and operational intelligence. The acquisition brings a telemetry-native, data lake-oriented observability platform directly into Snowflake’s core architecture, aligning logs, metrics, and traces with analytics, AI, and governance workflows. Observe’s platform was designed around open standards, high-volume telemetry economics, and AI-assisted troubleshooting, making it structurally compatible with Snowflake’s data-centric operating model. Financial terms were not disclosed, and the transaction remains subject to customary closing conditions. If executed effectively, this acquisition materially shifts Snowflake’s role in enterprise IT operations by collapsing the boundary between operational telemetry and business data, repositioning observability as a first-class data platform workload rather than a standalone monitoring function.

Rafay Systems released Version 4.0 of its infrastructure orchestration and workflow automation platform, introducing new capabilities aimed at strengthening governance, simplifying Kubernetes lifecycle operations, and accelerating cloud native workload delivery across hybrid and multi-cloud environments. The release delivers enhancements in managed Kubernetes services, developer self-service, secure GitOps workflows, blueprint lifecycle management, and workflow automation. These updates are designed to address the operational and governance burdens facing platform engineering teams as Kubernetes estates grow in scale and complexity. The 4.0 release solidifies Rafay’s position as a control plane for platform teams that need consistent, policy-aligned infrastructure delivery while enabling faster developer velocity and safer Day-2 operations.

Rafay Systems and Aviz Networks announced a partnership to deliver full-stack orchestration for GPU cloud environments, combining Rafay’s Kubernetes, GPU, and cluster lifecycle automation with Aviz’s AI-optimized networking fabric, visibility, and multi-tenant control. The joint solution targets enterprises and GPU cloud providers seeking to accelerate AI infrastructure deployment, reduce operational complexity, and enable secure, tenant-aware consumption of GPU resources. By integrating cluster orchestration, GPU lifecycle management, fabric-level automation, and correlated observability, Rafay and Aviz move beyond traditional provisioning toward a unified operational plane for AI workloads. The announcement highlights the market’s shift toward end-to-end AI infrastructure orchestration, where compute, networking, and lifecycle governance function as a coordinated system rather than isolated components.

In this episode of the Cybersecurity Awesomeness Podcast, Chris Steffen and Ken Buckler gear up for RSAC 2026, where 45,000+ attendees and hundreds of vendors will converge in San Francisco.From Agentic AI and autonomous security solutions to the growing urgency of Post-Quantum Cryptography, the conversation separates real priorities from industry hype. The hosts explore why AI isn’t a silver bullet, what leaders should actually be securing today, and how organizations can prepare for the coming reality of Q-Day.They also share practical advice for navigating the show floor, including why the Innovation Sandbox and Early Stage Expo remain must-visit destinations, and why, despite all the technology on display, community and face-to-face connection still deliver the greatest value.Tune in for insights before heading to RSAC.

How much of data trust can tools actually solve? It's a question EMA's Herb Blecher keep encountering, and the market isn’t structured to answer. Observability, quality, and governance are converging into a shared architectural layer, and the tooling is genuinely better. However, organizations that struggle with trust don't always lack tools; they have a gap between what their policies say and what their infrastructure enforces. No amount of platform consolidation can close that gap on its own.Read the blog to learn more.

Transforming the business does not require expensive technology changes, it can be accomplished through business partnership and better use of existing systems and information. In this episode of the Enterprise Automation Excellence podcast, Rich Corbridge (CIO, SEGRO plc) joins Dan Twing and Tom O'Rourke to discuss how they are using automation and AI to significantly improve their ERP capabilities, without replacing their ERP systems. Focusing on business value, SEGRO is piloting AI-enable orchestration to create workbenches that tie together disparate systems for timely access to information, improving decision making. Crucially, this approach introduces agentic AI within a centralized orchestration model, ensuring governance, consistency, and controlled deployment rather than uncoordinated agent sprawl.

On March 2, 2026, IFS finalized the acquisition of Softeon, launching IFS Softeon to bridge the gap between Industrial AI and tier-one warehouse management software (WMS). This move targets a critical enterprise blind spot: the disconnect between strategic ERP planning and the reality of the warehouse floor.

NinjaOne has introduced NinjaOne IT Asset Management (ITAM), a new capability that unifies endpoint and asset management in a single platform. NinjaOne ITAM gives IT teams a continuously updated inventory across hardware, software, connected systems, unmanaged devices, and offline assets. By centralizing asset data and tying it directly to endpoint control, NinjaOne aims to help organizations extend asset lifecycles, reduce costs, strengthen security, and simplify compliance. The new offering turns static asset records into operational intelligence that supports budgeting, risk reduction, and faster IT decision-making at scale.

ScienceLogic recently launched Skylar Advisor™, a new AI-native offering within the ScienceLogic AI Platform. This technology helps IT teams shift from reactive operations to proactive, evidence-based decision-making. Unlike retrofitted AI chatbots, Skylar Advisor applies real-time reasoning to enterprise telemetry and institutional knowledge. It delivers verifiable recommendations that accelerate issue resolution, reduce operational risk, and preserve institutional memory. Built for full-stack visibility and trusted automation, Skylar Advisor enables IT teams to prioritize critical tasks, act with confidence, and scale expertise across diverse roles.

EmberOT recently announced significant updates to its free OT PCAP Analyzer, now including support for the PCAP next-generation (PCAPng) file format. This updated version enhances the tool’s capabilities, offering improved performance, error handling, and a more comprehensive analysis of network traffic, which is critical for those working within industrial control systems (ICS). The introduction of PCAPng support allows analysts to leverage detailed packet insights, ultimately aiding in the security and efficiency of critical infrastructure operations.

BigID and Atlan have announced an enhanced integration that brings structured and unstructured data discovery, classification, lineage, and cataloging together under a combined governance and security control plane. The integration feeds BigID’s data security posture management (DSPM) signals directly into Atlan’s data catalog, giving security and governance teams a shared operational surface. The goal is to give CISOs and CDOs a common foundation so enterprises can advance AI initiatives from experimentation to production without governance becoming the bottleneck.

In this episode of the Cybersecurity Awesomeness Podcast, Chris Steffen and Ken Buckler prepare for the RSA Conference (RSAC), often described as the "Super Bowl" of cybersecurity, by talking about the EMA Vendor Vision report. To help attendees navigate the overwhelming presence of over 600 exhibitors, the hosts break down EMA’s "Vendor Vision" report, which spotlights ten essential innovators. The discussion covers a broad technological spectrum, ranging from Straker’s cutting-edge adversarial AI in the Early Stage Expo to Sky High Security’s leadership in Data Security Posture Management (DSPM).Key highlights include AWS’s unified cloud security suite, Acalvio's deception technologies, F5’s API-driven AI protections, and the evolving identity landscape spearheaded by Yubico and SailPoint. The hosts also examine the maturation of Privileged Access Management through Delinea and Keeper Security, alongside Proofpoint’s focus on human-centric vulnerabilities and business email compromise. By filtering the noise of the Moscone Center, this episode provides a strategic roadmap for identifying the technical trends that will define the industry for the coming months. It serves as an indispensable guide for anyone looking to maximize their impact and insight during the conference.

Hex built a collaborative data science notebook that became structurally aligned with LLM workflows — and leveraged that advantage to rapidly expand into conversational self-service, agent governance, and enterprise analytics within a single governed environment.

In this episode of the Don’t Panic, It’s Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Herb Blecher, Research Director of Data and Analytics at Enterprise Management Associates (EMA).This conversation challenges a common belief in enterprise tech – that gathering everything ensures insight. Blecher, alluding to the modern-day AI craze, cautions the enterprise audience that just because you can access vast amounts of unstructured data doesn’t mean you should.

FINBOURNE Technology and Alkymi have announced a strategic partnership delivering an integrated credit risk monitoring solution for private credit. The joint offering connects Alkymi’s AI-powered document ingestion and monitoring platform with FINBOURNE’s LUSID data and investment management infrastructure. The combined solution enables asset managers, lenders, and asset owners to detect deteriorating credit facilities earlier by integrating borrower documents, financial data, and covenant monitoring in real time.

Ataccama has launched Agentic Data Observability within Ataccama ONE, embedding pipeline monitoring directly into its unified data trust platform. The release connects observability signals with data quality rules, lineage, catalog, and governance in a single workflow. Pipeline monitoring spans orchestrators including dbt, Airflow, Dagster, Azure Data Factory, and AWS Glue. Ataccama also introduced an MCP Server that extends governed data access to AI tools such as Claude and Microsoft Copilot. The release is particularly relevant for regulated enterprises, where data integrity must hold up under audit and AI deployments create additional compliance risk.

ElastiFlow, a provider of network telemetry solutions, announced general availability of a Kubernetes observability technology called Mermin. Mermin leverages the Linux kernel’s extended Berkeley Packet Filter (eBPF) to extract network flow data from Kubernetes clusters.

With version 23 of its network observability product, Entuity Software™ introduced significant enhancements to how it collects data from IT infrastructure and services. This update gives IT operators more flexibility in how they collect data, a more secure approach to monitoring operating systems, and new options for collecting real-time network telemetry.

BlueCat Networks is introducing Horizon, a set of SaaS-based platform and infrastructure services that unifies its core DNS, DHCP, and IP address management (DDI) solutions with the network observability solutions it acquired via LiveAction Networks.

Enterprise Management Associates (EMA) has released its fifth annual Vendor Vision report, highlighting ten trailblazing cybersecurity companies to watch at the 2026 RSA Conference in San Francisco. Curated by analysts from the EMA Information Security practice, the report serves as a strategic guide for navigating the event's 600+ exhibitors.The 2026 visionaries are recognized for their innovation in securing agentic AI and protecting the autonomous enterprise against machine-speed threats. By identifying industry-leading solutions across diverse security verticals, EMA provides attendees with a comprehensive roadmap to the most significant technological advancements showcased at the Moscone Center.

AI adoption in cybersecurity is hitting a reality check.In this episode of the Cybersecurity Awesomeness Podcast, hosts Chris Steffen and Ken Buckler examine how CISO priorities are shifting as organizations move from rapid AI experimentation to a more disciplined, risk-aware strategy.The “deploy first, secure later” approach to AI—especially autonomous or agentic systems—is proving risky. Real-world incidents already highlight the dangers:

An AI agent deleting a production database during a code freeze
Another repeatedly ignoring “stop” commands while wiping a Meta executive’s inbox

As the hosts put it, AI can feel like “graduate-level intelligence with the gullibility of an eight-year-old.”The takeaway: marginal productivity gains aren’t worth catastrophic security risks.CISOs must apply the same guardrails used for human users—especially around identity, access, and accountability—to AI agents. Done right, AI becomes a powerful business enabler. Done wrong, it becomes a liability.

Enterprise automation leaders must maintain reliable services supporting business and operational processes while also driving change in their organizations. In this episode, Dan Twing and Tom O'Rourke discuss the challenges and opportunities of becoming a change agent, and why it is important to step forward rather than waiting for direction from above. They advocate behaving like an entrepreneur in identifying problems to be solved, building business cases, and actively selling their proposals to leadership.Key Points

Many automation groups only get attention when something is broken, functioning as a service rather than being recognized as a strategic asset
Senior managers often do not understand how automation enables the business
Automation leaders often wait for someone above them to provide direction on when to act
No matter where you are in the organization, you have a role to play in moving change forward
Automation leaders need to behave like entrepreneurs, driving change to improve their business impact
Marketing and selling are not words; automation leaders must frame proposals in business terms, understand executive and stakeholder priorities, and actively communicate the value automation delivers.

Takeaways for Automation LeadersCreate a "State of Automation" briefing that your management and non-technical stakeholders can read to understand why automation matters to the businessMeet with your management to understand: - their expectations of what the automation team needs to work on, - what business initiatives automation should be performing, and - whether the leadership team feels the automation team is moving quickly enoughDesign and propose a small pilot where automation can deliver a measurable business improvement

As AI reshapes network operations, enterprise teams increasingly expect intelligent capabilities embedded within their infrastructure and management platforms. Yet while demand for AI-driven NetOps continues to grow, trust remains the defining factor for adoption. Research from Enterprise Management Associates (EMA) shows that although AI heavily influences vendor selection, only a small percentage of IT professionals fully trust the AI tools managing their networks.For agentic NetOps to succeed, AI must be transparent, explainable, and verifiable. Network operators need clear insight into how AI systems generate recommendations through data attribution, visualized reasoning, and manual confirmation workflows. Trust is not about perfection; it’s about visibility, accountability, and continuous improvement. Organizations that prioritize these principles will empower their teams to confidently embrace AI and unlock faster operations, reduced errors, and smarter infrastructure management.

Quantum computing is rapidly moving from theoretical possibility to operational reality, and its impact on cybersecurity could be profound. In this episode of the Cybersecurity Awesomeness Podcast, EMA's security experts Chris Steffen and Ken Buckler examine the concept of Q-Day, the moment when quantum processors may become powerful enough to break today’s encryption standards, including AES-256.

The discussion explores the growing threat of “Harvest Now, Decrypt Later” attacks, where adversaries collect encrypted data today with plans to unlock it once quantum capabilities mature. While quantum technology promises transformative advances across industries — the so-called Quantum Dividend — it also introduces urgent security challenges that organizations must begin addressing now.

Listeners will gain insight into why quantum readiness requires a long-term strategy, how quantum-resistant cryptography is reshaping security planning, and why preparing for a post-quantum world is becoming essential for protecting sensitive data, intellectual property, and national security interests.

ServiceNow has agreed to acquire Pyramid Analytics, a decision intelligence platform that unifies data preparation, business analytics, and data science in a single environment. The acquisition targets a persistent gap in enterprise workflows: the disconnect between analytical insight and operational action. By embedding Pyramid’s capabilities into its platform, ServiceNow enables users to move from asking questions to triggering workflows without switching tools.

This is the research summary for EMA’s recent report, “DDI Directions 2026: Preparing Core Network Services for an Agentic, Multi-Cloud World.”

This research report explores how enterprises implement and operate solutions for DNS, DHCP, and IP address management (DDI), the core network-addressing technologies that IT organizations depend on for efficient, secure, and resilient network communications. Based on a survey of 300 IT professionals, this report identified DDI best practices and key challenges.

Artificial intelligence is fundamentally reshaping how organizations build, manage, and govern data and analytics systems. While no-code and self-service platforms once promised democratization, many enterprises discovered that ease of use often came at the cost of governance, consistency, and trust.

Today, AI is changing the equation. As large language models generate and reason about code, “as-code” architectures — once limited to highly technical teams — are becoming the foundation for scalable, auditable, and AI-ready operations.

In this blog, EMA Research Director Herb Blecher explores why AI is accelerating the shift toward declarative, version-controlled systems across the modern data stack, from transformation and observability to analytics and orchestration. Readers will learn how AI agents interact with structured environments, why text-based systems are inherently more compatible with AI, and how organizations can balance self-service usability with the governance required for trusted automation.

Lightrun has announced what it describes as the industry’s first AI Site Reliability Engineering (AI SRE) capability built on live, in-line runtime context. The platform enables engineering and operations teams to expose deeper execution-level signals directly within running systems, allowing investigation, hypothesis testing, and remediation validation without redeployments or permanent instrumentation changes. Rather than relying solely on static telemetry or pre-instrumented observability data, Lightrun’s approach allows teams to dynamically increase runtime visibility when deeper understanding is required and then return systems to their optimized operational state. While positioned within the emerging AI SRE category, the announcement reflects a broader shift in how organizations manage the longstanding trade-off between operational efficiency and semantic depth in software systems.

In this episode of the Cybersecurity Awesomeness Podcast from Enterprise Management Associates (EMA), cybersecurity experts Chris Steffen and Ken Buckler explain how quantum computers differ from traditional systems, what qubits are, and why post-quantum cryptography (PQC) is rapidly becoming a priority for enterprises, governments, and financial institutions. They explore how advances from major technology players such as Google signal accelerating progress and why organizations must prepare now for the potential disruption quantum computing could bring to modern encryption.

The discussion covers the “harvest now, decrypt later” threat, the concept of “Q-Day,” and what security leaders should be thinking about today to protect sensitive data in a post-quantum world.

Whether you’re a security practitioner, IT leader, or technology strategist, this episode delivers a clear, practical introduction to one of cybersecurity’s most transformative emerging challenges.

The current hype about AI has left many enterprise automation leaders feeling anxious about how they will integrate these rapidly evolving AI solutions whilemaintaining reliability and resilience. Hosts Dan Twing and Tom O'Rourke recommend taking a pause to recognize that there are more similarities than differences with past waves of integrations of new technologies into automation platforms. These integrations succeeded by following proven change management practices: start with small pilots, iterate based on learnings, collaboration with stakeholders, and maintaining production discipline.

The enterprise networking landscape is currently undergoing its most significant architectural shift since the inception of the internet. For years, the industry operated under a clear division of labor: networking teams focused on the “plumbing” of connectivity and bandwidth, while security teams built defensive perimeters around centralized data centers. Today, that bifurcated model has been rendered obsolete by the dual forces of cloud ubiquity and a fundamentally distributed workforce.

Zenarmor offers a unique secure access service edge (SASE) solution that maximizes performance and flexibility through an efficient network architecture.

Join EMA security experts Chris Steffen and Ken Buckler for this Cybersecurity Awesomeness podcast, where they discuss government approaches to cybersecurity, recruitment, and the impact of withdrawing from major conferences like RSAC on skills, technology adoption, and public-private collaboration. Learn how these shifts affect cybersecurity strategy, workforce development, and industry partnerships.

Listen now to explore expert insights and stay ahead in the evolving cybersecurity landscape.

DDI technology (DNS, DHCP and IP address management) serves as the control plane for the world’s networks. Theoretically, DDI technology helps network engineering teams manage IP address space and domain names, two foundational addressing technologies that enable communications across the internet and all private networks. DDI solutions should help network teams scale out, automate, and secure management of these core network services. Unfortunately, only 35% of enterprise IT organizations believe that they are completely successful with their DDI technology.Enterprise Management Associates (EMA) believes that enterprises must improve their approach to DDI if they are to succeed in a world where networks are becoming more complex, more multi-cloud, and AI-driven. Thus, EMA has published new market research, “DDI Directions 2026,” based on a survey of 300 IT decision-makers. This in-depth study offers essential guidance to enterprises on how best to implement, manage, secure, and integrate DDI solutions.Join EMA Vice President of Research Shamus McGillicuddy for a free webinar that will present key findings from this report, including:

Why only 28% of enterprises believe their DNS infrastructure is completely secure, and what you should do about it
Why high-quality APIs are an essential DDI capability, and what you should do with them
How you can ensure that DDI infrastructure and operations are consistent and reliable across hybrid, multi-cloud networks
What role DDI solutions play in establishing a network source of truth for mature network automation

BMC Software and Digitate have announced a joint product solution delivered via the AWS Marketplace that commercially bundles Digitate’s ignio™ AIOps platform with BMC Control-M, delivering enhanced anomaly detection, telemetry correlation, root-cause analysis, and observability-driven remediation capabilities for enterprise workflow orchestration. The solution strengthens the intelligence layer feeding Control-M–orchestrated remediation and response processes rather than introducing new agentic execution capabilities; BMC has long delivered event-driven automation and response workflows across its portfolio. Instead, ignio enhances the quality, speed, and trustworthiness of diagnostic insight that informs automated operations. EMA research shows that 70% of enterprises plan to adopt AI-powered orchestration within the next 12 months, while 86% already view orchestration as critical to digital transformation success. In that context, advanced observability analytics that safely enable scaled automation are becoming a competitive requirement, positioning this joint solution as timely and strategically relevant for BMC’s orchestration customers. The offering leverages AWS Marketplace’s recently introduced bundle capability, enabling customers to purchase the combined solution through a single Marketplace transaction while AWS allocates revenue to BMC and Digitate for their respective product components.

Cisco announced an integrated solution based on its Nexus data center switching platform and the Intel® Gaudi® 3 accelerator. Cisco’s Nexus 9364E-SG2 switches and OSFP-800G-DR8 transceivers are certified to support AI server clusters equipped with the Gaudi 3 accelerators, Intel’s latest alternative to GPU accelerators.

ServiceNow has announced a multi-year strategic agreement with OpenAI to deliver integrated, frontier-model capabilities directly within its AI Platform. The collaboration empowers enterprise customers with native access to OpenAI models, including GPT-5.2, enabling more natural, multimodal, and agentic AI experiences across workflows. The agreement also establishes joint engineering between ServiceNow and OpenAI to develop pre-built AI agents, speech-to-speech interactions, and automated orchestration of enterprise systems, eliminating the need for bespoke development. With this move, ServiceNow positions its platform as a secure, governed, and scalable control tower for enterprise-wide AI execution.

The IBM Consulting Application Management Suite for SAP (ICAMS) represents a significant advancement in SAP application management by leveraging generative and agentic AI technologies. Developed to meet the evolving needs of modern businesses, ICAMS not only enhances operational efficiency but also serves as a strategic enabler for organizations looking to navigate complex SAP transformations. This report delves into the context of SAP modernization challenges, highlights the key features and ramifications of ICAMS, and provides EMA’s perspective on its impact within the IT landscape.

Dynatrace acquired DevCycle to extend its observability platform into feature delivery and progressive release management for cloud-native and AI-driven applications. Built on the OpenFeature standard, DevCycle adds enterprise-grade feature flagging and experimentation capabilities that integrate directly with Dynatrace’s existing telemetry, causal analysis, and automation engine. The combined platform enables teams to connect feature intent, runtime behavior, and business outcomes in real time. This move advances Dynatrace’s strategy to evolve observability from passive monitoring into an active system of control that governs how features roll out, how risk is mitigated, and how performance and reliability align with business objectives.

Atomicwork has announced a strategic partnership with Lansweeper to enhance IT visibility in enterprise environments. The integration of Lansweeper’s real-time asset discovery with Atomicwork’s AI-native IT Service Management (ITSM) platform aims to provide seamless visibility across managed devices and network infrastructures. This collaboration enables enterprises to leverage the Universal Context Management Data Lake (CMDL), combining insights from various systems to proactively detect issues and streamline IT operations. The resulting synergy is designed to foster smarter IT management through improved contextual intelligence, ultimately aimed at reducing downtime and optimizing asset management efficiency.

Keeper Security has launched a new integration with ServiceNow® IT Service Management (ITSM) and Security Incident Response (SIR) to streamline the detection, investigation, and response to privileged access incidents. The integration allows security and IT operations teams to ingest real-time alerts from Keeper’s zero-knowledge, zero-trust privileged access platform directly into ServiceNow workflows. By enabling automated ticket creation, contextual enrichment, and secure event ingestion, the offering helps enterprises improve visibility across the identity layer and reduce response time for credential-based threats. The integration is now available in the ServiceNow Store.

NetApp has introduced major enhancements to its Intelligent Data Infrastructure portfolio with the launch of AFX and the NetApp AI Data Engine (AIDE). Together, these technologies form a unified architecture that combines high-performance storage with intelligent AI data orchestration. This launch signals NetApp’s strategic pivot from a storage provider to a full-stack data infrastructure partner, focused on accelerating enterprise AI deployment across Azure NetApp Files (ANF), Amazon FSx for NetApp ONTAP (FSxN), and Google Cloud NetApp Volumes (GCNV).

As data observability, quality, and governance tools increasingly rely on the same underlying architecture, long-standing market categories are beginning to converge around a single outcome: data trust. In this blog, EMA Research Director Herb Blecher examines why these domains are collapsing into a unified framework, what the convergence means for practitioners and vendors, and how platform providers are raising the bar for independent tools. Using a data trust convergence matrix, the post explores emerging competitive dynamics, the growing role of lineage, and why trust—not features—is becoming the defining metric in modern data platforms.

In this episode of the Cybersecurity Awesomeness Podcast, EMA VP of Research Chris Steffen is joined by Simon Wijckmans, founder and CEO of cside, to explore the growing risks of client-side security and why organizations have long overlooked the browser as a critical attack surface. The conversation examines how modern web architectures, third-party scripts, and marketing tools create hidden exposure—and why recent changes in browser technology and PCI DSS requirements are finally making effective client-side security possible. Together, they break down the limitations of legacy approaches, the emergence of proxy-based detection, and what security leaders should consider when incorporating client-side protection into their broader security strategy.

Enterprise IT continues to evolve rapidly as organizations look for effective ways to use AI and other automation tools to streamline their processes. In this episode, Dan Twing and Tom O'Rourke discuss Tom's learnings from December's Gartner IT Infrastructure, Operations & Cloud Strategies Conference. Topics include the emerging pragmatism around AI, continuous operations, managing the automation portfolio as a platform, and the importance of communicating automation value in business terms. The episode reinforces the need for automation leaders to focus on business outcomes and maintain reliability as the technology landscape changes.

In this blog, EMA Research Director Parker Hathcock shares the experience and philosophy that led him from product marketing into the analyst ranks. Drawing on more than fifteen years working across IT automation, observability, and service delivery, he explains why effective research must focus on people, outcomes, and execution—not just technology. The blog outlines his ServiceOps focus, the core pillars shaping the market, and his commitment to producing research that bridges real-world enterprise challenges with vendor strategy and decision-making.

This research report from Enterprise Management Associates (EMA) provides a comprehensive analysis of the current state of Identity Governance Administration (IGA), drawing on data from 135 IT decision-makers and practitioners. The study reveals a critical "execution gap" in the market: while improving security posture remains the primary driver for IGA adoption, 30% of organizations have been forced to abandon or scale back their Zero Trust programs due to persistent operational challenges.The report examines the friction between the industry's "cloud-first" narrative and the reality that 55.6% of enterprises still embrace on-premises or private cloud infrastructure. It further identifies "overprovisioning and privilege creep" as the top immediate risk—outweighing insider threats by more than double—highlighting a widespread struggle with access sprawl. Finally, the research charts the industry's aggressive pivot toward modernization, analyzing how AI-driven automation and improved integration capabilities are becoming essential requirements for the 59.3% of organizations currently transitioning to modern identity fabrics.

Explore EMA research on the 2025–2026 device management shift—from fragmented visibility to AI-native, autonomous fleets and Linux management parity.

In this episode of Cybersecurity Awesomeness Podcast, EMA cybersecurity experts Chris Steffen and Ken Buckler unpack a real-world AI-driven scam that went spectacularly wrong. After Ken receives a phishing email that accidentally includes the scammer’s own source code, the discussion reveals how attackers are using generative AI to create more convincing fraud and where those efforts still fail. The episode explores what this mistake exposes about modern scam tactics, the growing role of AI in cybercrime, and why organizations must adopt AI-powered defenses to keep pace with increasingly sophisticated, multimodal attacks.

On November 18, 2025, Salesforce finalized its acquisition of Informatica, adding enterprise-grade data cataloging, integration, governance, quality, privacy, metadata management, and master data management (MDM) to its platform. Salesforce positions Informatica as the data foundation for Agentforce 360, enabling governed, context-rich, and scalable agentic AI across the enterprise. The company plans to rapidly embed Informatica’s stack into Agentforce 360 and Data 360, deepen integration with MuleSoft, and enhance Tableau with richer insights. These moves strengthen Salesforce’s ability to deliver trustworthy autonomous experiences while giving customers a clearer path to agent-ready data.

ServiceNow has finalized its acquisition of Moveworks, enhancing its AI-driven capabilities by integrating Moveworks’ intuitive front-end AI assistant and enterprise search into its existing AI Platform. This strategic move aims to redefine employee engagement and streamline workflow processes across the enterprise. The collaboration between these two technologies promises to deliver advanced AI experiences that thoroughly connect employee requests to autonomous fulfillment, thereby fostering productivity and efficiency on a larger scale.

Forward Networks, a provider of network digital twin technology, enhanced its platform with a new agentic AI capability that allows users to explore and understand complex issues via a conversational interface. Forward AI is fully integrated into the mathematically accurate digital twin platform and has access to all the behavioral network data and features of that platform.

As AI and automation scale, data quality becomes more critical—but also more human. This blog explores why “the data isn’t ready” has always been true, why AI raises the cost of being wrong, and how confident machines amplify imperfect inputs. Moving beyond tools and observability, it argues that data quality is ultimately an organizational and governance challenge, requiring clear ownership, escalation paths, and human judgment embedded directly into data workflows.

EMA's cybersecurity analyst Ken Buckler received what appeared to be a routine investment scam email, only to discover the scammer accidentally sent their own AI-generated Python source code instead. This blog breaks down how “vibe coding,” placeholder SDKs, and misunderstood AI tooling exposed the scammer’s entire operation, turning fraud into farce. Beyond the humor, it serves as a sharp warning: using AI-generated code without understanding or review can leak secrets, undermine security, and cause real damage, especially in enterprise environments.

If you are responsible for managing enterprise automation, you already know that automation itself is not the problem. Most large organizations already rely on orchestration and automation to deliver their traditional ITOps scheduling, DataOps/MLOps and DevOps workloads across mainframe, distributed and cloud environments. The challenge is managing growing complexity, especially when automation issues span multiple platforms and execution engines. Advances in analytics and AI are changing what’s possible. By advancing observability beyond individual schedulers and applying intelligence to automation data, enterprises can move from reactive troubleshooting to faster diagnosis, near-real-time adjustment, and more confident decision-making at scale.Based on research from Enterprise Management Associates (EMA) and real-world enterprise use cases, this webinar will examine how organizations are making automation smarter—not by replacing existing tools, but by improving observability and analytics to better anticipate risk, understand impact, and achieve better automation outcomes.Watch this on-demand webinar featuring Dan Twing, President and COO at EMA, and Jon Hiett, Global Solution Architect at Broadcom Inc., for insights on:

Why automation complexity—not automation failure—is the real challenge at enterprise scale
Why expanded observability improves diagnostics everywhere but delivers its greatest value when automation issues span multiple platforms
Where analytics and AI enable faster adaptation during execution
The critical difference between reactive job monitoring and proactive business assurance and how to prevent SLA breaches before they impact the business.

In this episode of the Cybersecurity Awesomeness Podcast, EMA cybersecurity experts Chris Steffen and Ken Buckler are joined by Jim LaRoe, CEO of Symphion, to examine one of the most overlooked threats in enterprise security: printer and IoT device vulnerabilities. While organizations focus heavily on servers, endpoints, and cloud infrastructure, printers—often operating with default configurations and little ownership—remain a major attack vector.

The discussion explores why printers are uniquely exposed within enterprise environments, how their IoT nature creates visibility and governance gaps, and what real-world threats organizations are facing today. LaRoe breaks down the root causes behind poor printer security adoption, including organizational silos, lack of accountability, and misconceptions about risk, while highlighting practical steps enterprises can take to bring print fleets and other IoT devices into a unified security strategy.

This episode is a must-listen for security leaders, IT professionals, and risk managers looking to close critical blind spots in their cybersecurity posture and better protect sensitive data across the extended enterprise.

As organizations race to automate data quality and observability with AI, many are overlooking the most critical factor in trustworthy data: human judgment. This blog explores why data analysts—often the people who know the data best—are essential to maintaining trust, context, and usability as data volumes and automation scale.

Drawing on EMA research and real-world examples, the piece argues that data quality is less about technical perfection and more about reducing confusion, misinterpretation, and friction between humans and data. It examines empathy as a core analytical skill, the risks of removing humans from decision-making loops, and why “human-in-the-loop” design is not a limitation but a necessity.

Ultimately, the blog makes the case that the best data quality tools don’t replace analysts—they amplify their judgment, contextual knowledge, and ability to anticipate how data will actually be used and understood.

Enterprise IT has embraced observability for infrastructure, networks, applications, and data pipelines, but one critical domain remains largely invisible: automation itself. As workload automation orchestrates mission-critical business processes across hybrid environments, this blind spot has become a strategic liability.

Red Hat announced Red Hat Developer Lightspeed, an AI-powered portfolio embedded into its Developer Hub and Migration Toolkit for Applications to accelerate modernization and developer productivity. The assistants generate documentation, test plans, design rationale, and AI-assisted refactoring during application migration. A bring-your-own-model policy allows use of private or approved LLMs to meet enterprise controls. Unlike generic copilots, Lightspeed is inserted at modernization control points where architectural decisions and refactoring occur, reinforcing Red Hat’s modernization-to-OpenShift funnel. The announcement expands the Lightspeed strategy (beyond Ansible) into the developer lifecycle and positions AI as an instrument for modernization and governance suitability rather than a generic productivity add-on.

Embrace acquired SpeedCurve, uniting two expert teams to advance a user-focused model of observability anchored in real-user impact. Embrace brings an OpenTelemetry-based foundation for mobile and web RUM, while SpeedCurve contributes a decade of web performance leadership, Core Web Vitals expertise, and mature synthetic monitoring that digital brands trust. Together, the companies create a unified experience layer that connects frontend, mobile, and backend data around user reliability, performance, and business outcomes. The acquisition strengthens Embrace’s position within the composable observability ecosystem and accelerates the industry’s shift toward experience-driven reliability.

Opti recently announced the release of its identity security solution through AI-native automation. This new solution aims to redefine enterprise identity management and address growing concerns related to security vulnerabilities. Opti’s approach will leverage advanced artificial intelligence technology to fortify enterprise identity systems, mitigating risks associated with identity theft, unauthorized access, and other malicious activities.

This is the research summary for EMA’s recent report, “AI-Driven NetOps: How Enterprises are Embracing Intelligent Network Management Solutions.”

Based on new research from Enterprise Management Associates (EMA) surveying 135 IT decision-makers and practitioners, this webinar explores the growing “execution gap” in today’s IGA programs.

According to new research from Enterprise Management Associates (EMA) and the 2025 EMA™ Radar for Workload Automation and Orchestration, enterprises are raising expectations around orchestration scale, governance, observability, and AI readiness.Join Dan Twing, President and COO of EMA, and Guy Eden, VP Product Management at BMC Software, for a focused review of Radar findings and market research, combined with BMC’s perspective on where orchestration is heading next.

As organizations accelerate the adoption of agentic AI, they are unknowingly creating a new and powerful security risk: an autonomous digital workforce operating beyond traditional controls. In this blog, EMA cybersecurity expert Ken Buckler explore the “Triple Threat” facing enterprises in 2026: agentic AI risk, identity governance gaps, and the growing visibility blind spot. You will learn how excessive AI agent privileges, outdated identity frameworks, and limited monitoring capabilities combine to create a new class of insider threat. You will also get practical guidance on applying Zero Trust principles to non-human identities, governing AI agents at scale, and restoring visibility into autonomous systems before security debt turns into a crisis.

Network infrastructure and operations teams are entering a period of rapid change. As enterprises accelerate investments in AI, cloud, and automation, the demands on network teams are growing and traditional approaches are no longer enough.

In this Mean Time To Insight podcase, EMA Vice President of Research Shamus McGillicuddy shares his top predictions for how network teams will evolve in 2026. Learn why strong data foundations, tighter cross-functional collaboration, and smarter tools will define the next generation of high-performing organizations, and how you can start preparing today to stay ahead of what’s coming.

This market research explores how IT organizations plan to use artificial intelligence to optimize, accelerate, and automate network operations. Based on a survey of 458 IT professionals across North America and Europe, it explores current engagement with AI-driven network management solutions and efforts to develop homegrown AI tools. It also explores emerging best practice for AI-driven network management adoption.

In this episode of the Cybersecurity Awesomeness Podcast, EMA security experts Chris Steffen and Ken Buckler examine the security and privacy risks highlighted at CES, with a focus on internet-connected consumer devices. From AI-enabled treadmills and smart appliances to Bluetooth-enabled TVs and toothbrushes, they discuss how convenience-driven connectivity expands the attack surface, enables data misuse, and often prioritizes monetization over security. The conversation emphasizes consumer awareness, IoT privacy risks, and the importance of understanding how personal data is collected, shared, and protected in an increasingly connected world.

As EMA marks 30 years, this post reflects on how the analyst role has evolved in an increasingly crowded, AI-driven technology market. It explains why independence, technical depth, human judgment, and experience matter more than ever, and how EMA helps buyers and vendors cut through noise to find clarity.

In Episode 27 of the Enterprise Automation Excellence podcast, Dan Twing and Tom O’Rourke reflect on insights from 26 episodes recorded in 2024 and 2025. Using AI-assisted analysis, they identify five dominant themes shaping enterprise automation today, share key lessons learned, outline plans for the podcast in 2026, and discuss new ways to help listeners navigate and apply the growing body of automation insights.

In this blog, Herb Blecher analyzes internet search trends to uncover where real momentum exists in the data and analytics market. By examining dozens of D&A terms through Google Trends and qualitative signals, he highlights rising focus areas like data quality, governance, and data products, while showing how infrastructure and AI hype are maturing. The result is a practical, data-driven view of how the industry is shifting from building stacks to running them reliably.

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