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ExtraHop Releases Machine Learning Service Addy

ExtraHop announced the general availability of its new machine learning service, Addy.

When paired with the ExtraHop analytics-first workflow recently introduced as part of version 6.2, IT teams can easily and rapidly investigate issues from high-level performance metrics to individual transactions to packets in a matter of clicks rather than hours.

ExtraHop Addy is a SaaS offering that observes and analyzes all digital interactions and applies machine learning to detect anomalies in real time, helping IT teams take a proactive, data-driven approach to supporting and securing the digital experience.

Using wire data from the ExtraHop platform, Addy builds continuous baselines for every device, network, and application, and then proactively detects and surfaces potential issues in the environment directly into the ExtraHop UI. Feedback from in-house and crowd-sourced domain expertise complement the core algorithm and heuristics, helping to reduce the number of false positives over time to surface only the most critical issues.

In beta for the last year, ExtraHop Addy has helped early access customers spot and resolve hundreds of anomalies, from reverse DNS lookups to the Dyn DDoS attack. As a result, Addy customers have seen increased uptime, improved performance, and better collaboration across teams.

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ExtraHop Releases Machine Learning Service Addy

ExtraHop announced the general availability of its new machine learning service, Addy.

When paired with the ExtraHop analytics-first workflow recently introduced as part of version 6.2, IT teams can easily and rapidly investigate issues from high-level performance metrics to individual transactions to packets in a matter of clicks rather than hours.

ExtraHop Addy is a SaaS offering that observes and analyzes all digital interactions and applies machine learning to detect anomalies in real time, helping IT teams take a proactive, data-driven approach to supporting and securing the digital experience.

Using wire data from the ExtraHop platform, Addy builds continuous baselines for every device, network, and application, and then proactively detects and surfaces potential issues in the environment directly into the ExtraHop UI. Feedback from in-house and crowd-sourced domain expertise complement the core algorithm and heuristics, helping to reduce the number of false positives over time to surface only the most critical issues.

In beta for the last year, ExtraHop Addy has helped early access customers spot and resolve hundreds of anomalies, from reverse DNS lookups to the Dyn DDoS attack. As a result, Addy customers have seen increased uptime, improved performance, and better collaboration across teams.

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...