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AI Ticket Analysis currently reports on recurring incidents, such as repeated circuit-down alerts, without the ability to drill down to identify the specific underlying source driving the recurrence. Users cannot determine which circuit, device, or application is most frequently associated with a given incident pattern directly within the analysis view. This enhancement would add drill-down functionality that traces a recurring incident category to its exact source, surfacing the most impacted device or a ranked list of associated devices. Providing this level of detail would help customers prioritize remediation efforts on the components driving the highest incident volume, reducing time spent manually correlating recurring alerts to root cause.
NetBrain currently does not provide a centralized library of best-practice guidance for common network change scenarios. Customers must rely on generic change templates without embedded rationale or standardized recommendations for well-known change types. This enhancement would introduce a Golden Change Library containing pre-built best-practice templates for common network change scenarios, including explanations of the underlying rationale, which customers could further customize and manage. Customers would benefit from consistent, guided change execution that reduces risk and improves quality without building this guidance from scratch.
AI Ticket Analysis currently requires users to manually gather and upload tickets for each customer account before analysis can be performed. This process becomes highly time-consuming for organizations managing many customer accounts on a single platform, as each account must be processed individually. This enhancement would introduce functionality to automate or schedule ticket imports directly from ITSM systems into AI Ticket Analysis, removing the need for manual gathering and uploading. Customers operating multi-tenant or managed service environments would benefit from continuous, streamlined ticket analysis across large numbers of accounts without repetitive manual effort.
NetBrain does not currently notify users when a feature, such as Layer 2 topology calculation, cannot function correctly because required underlying data has not been provided. This can leave users uncertain why a capability failed or produced incomplete results. This enhancement would introduce contextual system messaging that detects missing prerequisite data for a given feature and alerts the user before or during the attempted action, with guidance on what information is needed. Customers would gain clearer understanding of feature dependencies, reducing confusion and support inquiries related to unexplained functional gaps.
AI Ticket Analysis currently does not support direct export of ITSM ticket data to CSV format; only PDF export is available, requiring users to rely on XML or API-based JSON responses and manual conversion to obtain CSV output. This enhancement would add a native CSV export option for AI Ticket Analysis results, allowing ticket data to be extracted directly in a structured, spreadsheet-ready format. Customers would benefit from faster reporting and easier downstream data processing without needing manual conversion steps.
AI Ticket Analysis currently evaluates ticket volume based on frequency alone, without accounting for ticket priority. As a result, high-frequency but low-impact issues are treated the same as rare but high-priority incidents, obscuring the true business impact of recurring problems. This enhancement would add a priority-based breakdown to ticket analysis, categorizing results into high-frequency tickets and high-impact tickets based on priority level. Customers would benefit from more actionable insights that support better decision-making and automation planning by distinguishing between frequent low-impact issues and rare high-impact incidents.
AI Ticket Analysis currently requires AI to be enabled in order to derive insights from tickets, leaving no alternative path for customers who do not have AI enabled in their environment. This enhancement would introduce an offline, script-based alternative for ticket analysis, along with clearer enablement guidance, so customers can still perform ticket analysis and derive insights without relying on AI. Customers operating in environments where AI cannot be enabled would gain continued access to ticket analysis capability and value from the feature.
AI Ticket Analysis currently reports ticket volumes as aggregate counts only, without identifying which branch, application, or device each ticket is associated with. This limits visibility into where incidents are concentrated and how significant their operational impact is. This enhancement would extend AI Ticket Analysis to break down ticket data by branch, application, and device, allowing users to see not just how many tickets occurred but where and on what they occurred. Customers and their leadership teams would gain clearer visibility into the real business impact of recurring incidents, enabling more effective prioritization of remediation efforts.
Automation Insights currently displays intents and checks with labels that do not convey enough context, requiring users to open individual items to understand their purpose or to distinguish between similarly named entries, such as multiple instances of the same access list appearing across different devices. This makes it difficult to quickly identify relevant checks during troubleshooting or to determine whether repeated entries are distinct or duplicates. This enhancement proposes adding hover-over descriptions for each rule or check so users can understand its purpose without opening it, along with visual alert indicators on devices within the map view, such as a count badge showing the number of associated alerts. These additions would help users identify relevant automation resources and problem areas more quickly, reducing unnecessary navigation and improving efficiency during troubleshooting.
Offline discovery currently captures only basic device details, such as hostnames, and does not collect serial numbers, models, or other attributes that are available through SNMP-based discovery. This creates an inventory gap for customers relying on offline discovery as their primary collection method. This enhancement would leverage ADT and parsing techniques to supplement offline discovery results with additional device attributes, including serial number and model, bringing offline inventory closer to parity with SNMP-based discovery. Customers using offline discovery would benefit from a more complete inventory without needing to run follow-up SNMP scans or manually enter missing device details.
NetBrain currently requires users to configure Golden Config manually without any guided assistance, making the setup process difficult for customers who are less familiar with the underlying logic compared to the more intuitive Golden Intent workflow. This enhancement would introduce a guided, step-by-step interface for Golden Config creation that walks users through key decisions, such as whether their configuration variations are known, and directs them to the appropriate setup path accordingly. A complementary approach could overlay contextual, on-screen guidance directly within the feature panes for complex sub-features, with an option for experienced users to disable the guidance mode. Providing this guided experience would reduce the learning curve for new and infrequent users, decrease setup errors, and allow customers to adopt Golden Config independently without extensive support intervention.
NetBrain's Golden Assessment framework currently cannot fully represent certain configuration elements that are static and customer-specific, such as AAA or NTP server settings, nor can it capture relationships that are only identifiable from a single device's perspective, such as VRRP pairings. As a result, users and support teams must continue relying on manual or reverse-engineering workflows through Golden Config to capture and validate these elements. This enhancement would extend the Golden Assessment framework to natively support static, customer-defined parameters and relationship-based configuration constructs, reducing the scope of items that require the older Golden Config approach. Customers would benefit from a more unified assessment experience with less manual effort spent reconciling dynamic and static configuration validation methods.
NetBrain currently does not clearly support all GDR properties within control variables used to segment rules by site information, and when a needed property is unsupported, customers must modify behavior through a plugin, which carries risk of unintended side effects. This enhancement would extend control variable support to cover the full set of GDR properties natively, removing the need for plugin-based adjustments. Customers managing site-based rule segmentation would gain a more reliable and lower-risk way to configure rules, reducing dependency on custom plugin changes.
Rule Discovery currently focuses on dynamic, design-level insights and does not provide a way to identify and capture common static configuration values across a device population. As a result, customers cannot easily determine which configuration variations represent standard practice, such as identifying the most common NTP server settings used across an environment. This enhancement would introduce a reverse-engineering approach within Rule Discovery that analyzes existing configurations to surface the most frequent variations for a given setting and allow those to be defined as standard configuration rules. Customers would benefit from a faster, data-driven method for establishing golden configuration standards, reducing the manual effort required to review configurations individually and improving consistency across the environment.
NetBrain currently evaluates configuration consistency based on syntax and structure rather than functional equivalence, which can produce false inconsistency alerts when devices running different IOS versions or vendor platforms express the same access control logic in different formats. This enhancement would allow configuration comparison logic to focus on the functional outcome of an access control entry, such as the permit or deny decision and its associated traffic match criteria, rather than requiring exact syntactic alignment. Customers managing multi-version or multi-vendor environments would benefit from more accurate consistency validation and fewer unnecessary alerts caused by syntax differences that do not affect actual network behavior.
NetBrain currently lacks integration with log analytics and security orchestration platforms such as Splunk, ELK Stack, Grafana, or Phantom, limiting the ability to surface network context within those tools or trigger automated investigations from external alerts. This enhancement would establish integration points allowing NetBrain insights and map-based visualizations to be embedded in third-party dashboards, and enabling automated NetBrain investigations to be triggered from alerts generated in those platforms. Customers operating security and network operations teams would gain a unified, contextual view across fragmented observability and automation tools, reducing manual correlation effort and improving situational awareness during incident response.
NetBrain currently lacks native support for Juniper Session Smart Router (SSR) devices, which limits visibility and automation coverage for customers operating hybrid SD-WAN environments built on this platform. This enhancement would add SSR as a supported device type, enabling discovery, data collection, and inclusion in end-to-end path analysis workflows. Customers adopting Juniper SSR for SD-WAN would gain complete network visibility and automation coverage without relying on manual workarounds.
The Domain Accuracy Resolution interface currently contains grammatical inconsistencies and layout elements that reduce clarity and ease of use. This enhancement would involve reviewing and correcting text throughout the feature and refining the visual layout for improved readability and organization. A cleaner, more polished interface would help customers navigate and interpret domain accuracy information more intuitively, reducing confusion during troubleshooting workflows.
Device Scope configuration for Reference Cluster currently allows selection of only a single device group at a time, requiring administrators to consolidate devices into a new combined group when multiple groups need to be referenced together. This enhancement would allow multiple device groups to be selected simultaneously when defining a Reference Cluster scope. Customers managing devices across several logical groupings would benefit from reduced administrative overhead and would no longer need to create and maintain duplicate consolidated groups solely to work around the single-selection limitation.
NetBrain Runbooks currently do not provide a way to duplicate an existing node within a Runbook, including its full configuration such as CLI commands, golden config rules, intent settings with task variables, or configlet parsers. Users must manually recreate a node and re-enter all settings when a similar validation step is needed elsewhere in the same Runbook, such as when building pre- and post-change comparison steps. This enhancement would add a 'Duplicate Node' option within the Runbook editor, allowing a node and its configuration to be copied to a new node with minimal manual re-entry. Customers performing change validation workflows would save setup time and reduce configuration errors when replicating complex node settings across pre- and post-change steps.
When the 'Ignore Order of Lines' option is used together with masked or ignored keywords in a Golden Configuration match pattern, the comparison logic can fail to detect missing or extra configuration lines between the golden and target configs, since masking causes multiple lines to appear identical and order-independent matching treats them as present regardless of actual count or placement. As a result, no alert is triggered and the details view shows no configuration difference, even though a real discrepancy exists. This enhancement would add a secondary verification step to the alert condition logic specifically for checks using 'Ignore Order of Lines,' confirming that an actual line-count or content difference exists before suppressing an alert. Customers relying on order-independent, masked-pattern compliance checks would gain more reliable alerting and reduce the risk of undetected configuration drift.
Boolean Network Intent variables currently accept mixed-case values, such as "True" or "TRUE" alongside "true," without validation, and there is no error shown when a case variant is entered during variable creation. In addition, variable definition guidance instructs lower-case true/false while the diagnosis step presents a dropdown using upper-case True/False, creating inconsistency between the two areas. This enhancement would add validation to enforce a single consistent case format for Boolean variable values at creation time, and align the case format shown in variable definition with the case format presented in diagnosis. Standardizing case handling would reduce configuration errors and eliminate confusion for customers building and maintaining Network Intents with Boolean variables.
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