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NetBrain does not currently provide a native or certified adapter for integrating with the EcoStruxure platform, which limits visibility into environments that rely on this system for infrastructure or facility management data. This enhancement would add a supported adapter for EcoStruxure within the Integrated Edition, enabling discovery and modeling of associated devices alongside other supported platforms. Customers operating EcoStruxure environments would gain consistent, supported integration and improved network visibility without relying on manual workarounds or custom-built solutions.
NetBrain currently requires users to add ADT Intent columns to a Runbook one at a time, even when the desired intents originate from different Automated Data Tables. This enhancement would allow users to select and add multiple ADT Intent columns from different ADTs in a single action during Runbook configuration. Customers building Runbooks that reference several ADTs would benefit from reduced setup time and a more efficient configuration workflow.
The Path action node in Runbook automation currently accepts only source and destination as template variables, with no option to pass source port or destination port as inputs. This enhancement would extend the Path action node to accept source port and destination port as additional template variable inputs, alongside source and destination. Supporting full four-tuple input would allow automated, ticket-triggered path analysis to reflect the exact traffic flow being investigated, improving accuracy for customers running runbook-based triggered automation.
NetBrain currently does not provide a way to export database update log details for offline review or record-keeping. This enhancement would add an export function for the database update log, allowing administrators to save the log contents to a file. Customers responsible for maintaining update records and supporting audit or compliance reviews would benefit from being able to retain and share this information without manual data collection.
Dashboards currently support splitting data only by Device Site or Device Group, which limits how users can categorize and view alert data. When Device Groups are misconfigured or contain overlapping device membership, alert counts can be skewed or duplicated across groups, reducing the reliability of dashboard reporting. This enhancement would add native split options for Device Type and Geolocation, allowing users to categorize dashboard data along these dimensions without relying on manually maintained Device Groups. Customers would gain more accurate and flexible dashboard views, reducing the risk of misleading alert counts and simplifying dashboard configuration for organizations that categorize their network by device type or physical location.
Summary Dashboards currently display multiple independent numeric metrics per group, and when these roll up into executive-level views, the resulting large set of numbers can be difficult to interpret at a glance. There is no option to hide unnecessary metrics or to present health status through visual indicators rather than raw counts. This enhancement would allow summary metrics to be configured for visibility, including the ability to show, hide, or remove specific metrics, and would introduce visual summary widgets such as pie charts for success versus failure, bar or stacked bar charts for alert distribution, and trend arrows for status change over time. Executives and management-level stakeholders would gain a faster, more intuitive understanding of group-level health without needing to interpret dense numeric displays.
Dashboards currently ingest entire Golden Intent objects in full, including every diagnosis block they contain, without a way to isolate only the blocks relevant to a specific workflow. This enhancement would allow users to select specific diagnosis blocks from a Golden Intent and apply keyword or tag-based filtering, such as config drift from baseline, config drift from reference, state check, or config check. Customers running Config Drift workflows would be able to surface only the diagnostic results relevant to their use case, improving dashboard clarity and reducing noise from unrelated diagnosis blocks.
Observability dashboards currently incorporate all intent dashboards associated with a rule automatically, and administrators must rely on ADT-based selection to manage which intents appear, a method that does not scale well in large environments and is prone to error due to truncated intent column names. This enhancement would introduce a dynamic, searchable interface for selecting intent dashboards, with support for grouping by feature, intent type, and vendor. Customers managing large or evolving environments would benefit from reduced manual effort when new designs or vendors are introduced, along with more accurate and scalable dashboard configuration.
NetBrain's ServiceNow integration, when AI TAF is enabled, currently sends every incoming incident to Deep Diagnosis by default whenever no specific manual trigger definition exists, without any filtering based on incident content. This means incident data, including descriptions that may contain sensitive or personal information, can be forwarded for AI processing without customer control. This enhancement would introduce configurable filtering or an opt-in mechanism that lets customers define which incidents are eligible for automatic Deep Diagnosis processing, allowing exclusion of incidents likely to contain sensitive data. Customers in regulated industries, such as healthcare or human resources, would benefit from reduced risk of inadvertently transmitting sensitive information to third-party AI services, supporting their data privacy and compliance requirements.
NetBrain currently does not automatically add Automated Data Tables (ADTs) and Golden Intents delivered through library installations to a tenant's AI settings. As a result, administrators must manually configure these settings before Deep Diagnosis can recognize and use the newly installed content. This enhancement would allow ADTs and Golden Intents included in installed libraries to be automatically reflected in the tenant's AI settings at the time of installation. Automating this step would reduce manual configuration effort and allow customers to make immediate use of installed library content within Deep Diagnosis workflows.
NetBrain's API Server Manager does not currently support Barracuda Control Center as an API source type, which prevents discovery of devices managed through that platform. This enhancement would add Barracuda Control Center as a selectable API source type, enabling discovery and modeling of associated devices alongside other supported platforms. Customers running Barracuda-managed infrastructure would gain complete device visibility without relying on manual workarounds.
Network Intent currently requires users to manually expand or collapse each condition within a diagnosis individually, which becomes time-consuming when a diagnosis contains a large number of conditions. This enhancement would add Expand All and Collapse All controls at the diagnosis level, allowing all conditions to be opened or closed in a single action. Customers building or reviewing complex diagnoses with many conditions would benefit from reduced manual effort and faster navigation through diagnosis logic.
NetBrain currently adds discovered devices directly into the active inventory and applies them toward the licensed device count as soon as discovery completes, without an intermediate verification step. This enhancement would introduce a holding tank for discovered devices, allowing an administrator to review and explicitly approve devices before they are added to inventory and counted toward the device license. This capability would give administrators greater control over inventory accuracy and license consumption, reducing the risk of unwanted or unintended devices being counted against the license.
Pre and post checks for remediation actions currently support only command-based or template-based validation, with no option to include Network Intent as a check type. This enhancement would allow Network Intent to be configured as a pre and post check, so the intent logic can validate network state before remediation and confirm resolution after remediation completes. Running the intent as part of these checks would also ensure that dashboards and runbooks reflect updated and accurate results following remediation, giving customers greater confidence that automated actions produced the intended outcome.
NetBrain currently does not offer a consolidated view under System Management that presents audit logs and automation usage together across all domains and tenants; administrators must review this information separately and on a per-domain or per-tenant basis. This enhancement would introduce a single-pane dashboard that displays per-user audit log activity, with drill-down capability, alongside automation usage data spanning all domains and tenants. Administrators managing large multi-tenant and multi-domain environments would benefit from centralized visibility, reducing the manual effort required to compile activity and usage information for security review, compliance reporting, and operational oversight.
NetBrain's CLI Command Data Retrieval Rules currently permit or restrict commands based on keyword matching, such as checking for the presence of an included term like 'display.' This approach does not account for device-specific syntax, such as pipe operators, that can be appended to a permitted keyword to execute an otherwise restricted or higher-privilege command. This enhancement would strengthen the command matching engine to validate the full command string against configured rules rather than relying on keyword presence alone, preventing restricted commands from being executed through chaining or piping techniques. Ensuring accurate enforcement of CLI command restrictions would help customers maintain intended security boundaries and reduce the risk of unauthorized command execution in restricted access environments.
NetBrain currently relies on CLI-based data collection to retrieve routing and forwarding information from certain firewall devices during Live Path Discovery. When CLI-based retrieval is not supported or fails on such devices, the path discovery process stops at that device and cannot complete an end-to-end trace. This enhancement would allow NetBrain to use SNMP as an alternative data retrieval method for firewalls during Live Path Discovery, enabling the platform to collect the required routing and forwarding information when CLI access is unavailable or restricted. Customers operating in environments where firewalls are managed via SNMP rather than CLI would benefit from complete end-to-end path visibility without manual workarounds.
NetBrain currently requires multiple manual steps to collect the logs needed to troubleshoot AI-related issues, such as reasoning errors, summarization problems, or crashes. Support and engineering teams must gather logs separately from the GUI service monitor and AI settings, and additionally enable LangSmith logging to capture further diagnostic detail. This enhancement proposes adding a consolidated log collection option, similar in accessibility to the existing "Retrieve Live Data" action, that gathers required GUI and LangSmith logs in a single step for both AI Bot and Deep Diagnosis. Automatically saving AI Bot execution results when a map is saved, along with a quick-access "Export Logs" option, would further streamline the process. Customers and support teams would benefit from faster, simpler log collection, reducing the time and effort needed to diagnose AI-related issues.
The Inventory Report currently displays only the benchmark data captured at the time the report is generated, with no ability to view how values have changed over prior collection periods. This enhancement would add support for retaining and displaying historical interface data within the Inventory Report, allowing users to review value changes across multiple time points rather than only the most recent snapshot. Customers who rely on the Inventory Report for trend analysis and long-term monitoring would benefit from improved visibility into historical changes without needing to manually archive or compare separate report exports.
NetBrain currently does not provide an API to assign devices for visibility-only purposes on a map, a function only available today through the manual 'Add additional devices for Map' option in the user interface. This limits customers who need to apply this action at scale across many sites, requiring manual repetition for each map. This enhancement would expose an API endpoint that allows devices to be added to a map for visibility purposes in bulk, matching the capability of the existing manual option. Customers managing large environments with many sites would gain the ability to automate device visibility assignment, reducing manual effort and improving consistency across maps.
NetBrain dashboards are currently built around intents and per-site alert counts, and generating hierarchical operations views requires substantial manual effort since there is no way to automatically create a dashboard for each row of an Automated Data Table. This enhancement would add the ability to automatically generate a dashboard for every row in an Automated Data Table, enabling a progressive drill-down experience from a global view down to regional, sub-regional, and specific issue levels such as protocol state changes or configuration drift. Customers building network operations center style views would benefit from significantly reduced manual dashboard construction effort and a more consistent, scalable way to monitor network health across organizational hierarchies.
Today, follow-up Network Intents that have remediation enabled do not support distinct, intent-specific remediation templates; remediation logic is currently applied in a shared manner across follow-up intents. This enhancement would allow each follow-up intent to be configured with its own separate remediation template, tailored to the specific condition it diagnoses. Customers using multi-step diagnostic workflows with remediation enabled on follow-up intents would gain more precise and reliable automated remediation, reducing the risk of applying a mismatched or overly generic remediation action.
Change Template variables currently require manual entry each time a change is executed, with no option to select from existing config parameters or previously used values. This enhancement would allow variables to be populated by selecting from config parameters generated through Golden Config Rule Discovery, as well as from a list of saved or customer-added entries. This capability would reduce repetitive manual entry, improve consistency across change executions, and speed up the change remediation process.
The Runbook Document node currently allows granular selection of which runbook components to include in a Word export, but it does not provide any option to select or customize a Word template, such as styles, fonts, or branded layouts, in the way that the Map Export to Word feature does through its 'Select Style' option. This enhancement proposes adding Word template customization to the Runbook Document node, giving users the ability to choose from predefined styles or apply branded layouts consistent with other export workflows. Supporting this capability would help customers maintain consistent formatting and branding across all exported documentation, reducing manual reformatting after export.
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