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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.
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.
NetBrain currently attempts SNMP access against every IP address identified during a live network discovery, even when the IP does not respond to an initial ping check. In large-scale environments, this results in significant time spent on SNMP attempts against unreachable or non-existent hosts, extending overall discovery duration. This enhancement would introduce an option to skip SNMP attempts for IP addresses that fail a preliminary ping check during discovery. Customers running large seed-based or full-network discoveries would benefit from substantially reduced discovery times and more efficient use of scanning resources.
NetBrain path calculation currently does not reliably determine the correct traversed path when a VLAN is stretched across multiple sites or devices, unless the user manually specifies the gateway to use. Without manual intervention, the calculated path can diverge from actual traffic flow in these environments. This enhancement would extend the path calculation engine to automatically identify the correct gateway or next hop when tracing across a stretched VLAN topology, removing the need for manual gateway selection. Customers operating stretched VLAN architectures would gain accurate, automatic path visibility, reducing manual verification effort during troubleshooting.
NetBrain currently does not support scoping Azure discovery by individual resource groups, requiring administrators to discover resources at a broader subscription or tenant level. This enhancement would allow users to select specific Azure resource groups as the discovery scope, so that only resources within the chosen groups are included. Customers managing large or segmented Azure environments would benefit from more targeted discovery, improved organization of cloud resources, and reduced time spent mapping unnecessary infrastructure.
NetBrain's Golden Engineering Studio currently does not reliably reflect the true state of unused pattern lines and unmatched lines within a match pattern scenario, as the associated counters can increment even when no such lines are present in the scenario. This enhancement would ensure that unused pattern line and unmatched line counters are calculated and displayed accurately, matching the actual scenario content. Customers relying on these counters to verify configuration compliance and troubleshoot match pattern scenarios would gain accurate, trustworthy reporting without needing to manually cross-check the underlying scenario data.
The Word Document Content Template for Network Intent reports does not currently offer an option to include the parsed configuration snippet that an Intent references, requiring users to manually embed relevant config text within diagnosis messages instead. This enhancement would add a selectable field in the Content Template that inserts the parsed configuration segment associated with the Intent directly into the exported report. Customers who generate assessment or compliance reports would benefit from clearer, more complete documentation without needing manual workarounds to surface the underlying configuration data.
NetBrain's Runbook Compare node currently supports comparing data collected from the same device over time, but it does not provide a way to directly compare the output of two different devices against each other on a line-by-line basis. This enhancement would add an option to the Compare node allowing users to select two distinct devices and view their command output or table data compared side by side, line by line. This capability would help network teams quickly identify configuration or state differences between paired or redundant devices without manual cross-referencing.
NetBrain currently only supports exporting Runbook node results as a whole, requiring users to run a global export and then search through the full result set to locate data for a specific device or command. This enhancement would add contextual export options, accessible through right-click, that allow a user to export the retrieved data for a single device within a node, or the command output for a single command executed against a device, as an individual file. Providing this granular export capability would let customers quickly retrieve the specific data they need without navigating through large consolidated exports, improving efficiency for troubleshooting and documentation tasks that focus on individual devices or commands.
NetBrain currently does not provide a way to unpublish prompts within AI Bot once they have been published. This enhancement proposes adding an option to unpublish previously published prompts, whether individually or in bulk. Customers would benefit from improved control over prompt lifecycle management, allowing them to retract outdated or incorrect prompts without relying on manual workarounds.
The QApp alert node currently displays only the most recently triggered alert message, even when multiple alert conditions occur on a map, while the map itself correctly surfaces all triggered alerts. This enhancement would update the alert node to append and retain all triggered alert messages rather than overwriting with only the latest one. Customers relying on the alert node to track multiple simultaneous or sequential alert conditions would gain complete visibility into all triggered alerts, supporting more accurate monitoring and diagnosis.
NetBrain's Golden Assessment Library currently does not include validated assessment content for Cisco Firepower devices, leaving customers without out-of-the-box configuration compliance checks for this platform. This enhancement would extend the Golden Assessment Library to natively support Cisco Firepower devices, enabling standard assessment and compliance checks through Deep Diagnosis and Runbook Companion workflows. Customers operating Cisco Firepower devices would benefit from consistent, supported assessment coverage without needing to build and maintain custom checks.
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