What’s New in AutomaticSearch Investigator 2.5 — Key Updates & ImprovementsAutomaticSearch Investigator (ASI) 2.5 introduces a substantial set of improvements across accuracy, speed, usability, integrations, and security. This release focuses on making analysts’ workflows faster and more reliable while reducing noise and maintenance overhead. Below is a deep dive into the most important changes, what they mean for users, and practical tips for getting the most from the new version.
Summary of the headline improvements
- Improved query relevance and ranking through a revamped ranking model and expanded feature signals.
- Faster index updates and search latency reduction via an optimized ingestion pipeline and caching layer.
- New modular connectors and richer integrations for common data sources and platforms.
- Enhanced UI/UX and investigator workflows with layout changes, saved-workspace features, and smarter filtering.
- Stronger privacy, auditability, and compliance controls including role-based access and immutable audit logs.
- More flexible automation and orchestration with user-friendly scripting and a new rule engine.
Relevance and Ranking: Smarter Results
What changed
ASI 2.5 introduces a redesigned ranking system that combines classical information-retrieval signals with newer contextual and behavioral signals. Key changes include:
- Expanded feature set for ranking (semantic similarity, temporal relevance, user feedback signals).
- A newly trained ranking model tuned on real analyst interactions and labeled relevance judgments.
- Relevance personalization options that learn from saved searches and accepted/declined results.
Why it matters
Search results are now more likely to surface actionable documents earlier, reducing time spent scanning irrelevant items. The combination of semantic embeddings and interaction signals helps surface items that are contextually relevant even if they don’t share exact keywords.
Practical tip
Enable “personal relevance” in user settings for your team leads to prioritize results that reflect how senior analysts interact with cases. Use the new relevance diagnostics panel to see which signals influenced a result.
Indexing, Ingestion, and Performance
What changed
- An asynchronous, chunked ingestion pipeline replaced the older synchronous approach, enabling near-real-time index updates for large datasets.
- Incremental indexing now avoids full re-indexes for many schema changes.
- A new distributed caching layer reduces repeated computation for popular queries.
- Query execution plans are now optimized with cost-based heuristics.
Why it matters
Large-scale collections now refresh faster and queries return quicker under typical workloads. This lowers the time between data ingestion and detection/alerting, improving responsiveness for investigations.
Practical tip
For heavy ingestion workloads, set batching thresholds in the ingestion configuration to balance latency and throughput. Enable the monitoring dashboard to track ingestion lag and cache hit rates.
Integrations and Connectors
What changed
ASI 2.5 ships with an expanded connector library and a modular connector framework:
- New out-of-the-box connectors for cloud storage (S3 variants), modern collaboration platforms (team chat and enterprise social), and common SIEMs.
- A connector SDK that simplifies building custom adapters with fewer lines of code and prebuilt templates for paging, rate limiting, and retries.
- Improved OAuth and token management support for modern APIs.
Why it matters
Easier access to diverse data sources lowers engineering effort and shortens time to value. The SDK reduces connector maintenance and helps keep data pipelines robust to API changes.
Practical tip
Use the connector templates for incremental sync and enable the built-in retry/backoff policy. Test new connectors in the sandbox environment before promoting them to production.
UI/UX and Investigator Workflows
What changed
- Redesigned results layout with adaptive panels optimized for triage and deep-dive investigation.
- Saved workspace functionality that persists panel arrangements, active filters, and open artifacts per case.
- Bulk action improvements: multi-select export, labeling, and triage workflows.
- New visual filters: timeline scrubber, entity heatmaps, and quick-facet pill filters.
- Keyboard-driven navigation and accessible shortcuts to speed repetitive tasks.
Why it matters
Analysts spend less time configuring views and more time analyzing evidence. The saved-workspace feature enables consistent setups across teams and reduces onboarding time for new investigators.
Practical tip
Create and share workspace templates for common case types (fraud, IP infringement, insider threat). Use keyboard macros for frequent triage actions to shave minutes off repetitive tasks.
Automation, Scripting, and Rule Engine
What changed
- A new rule engine with a visual editor for composing detection and enrichment rules.
- Scriptable actions in Python and a sandboxed execution environment for custom enrichment and transformation.
- Orchestration hooks for triggering downstream workflows (ticket creation, alert notifications, external API calls).
- Versioning for automation scripts and rollback capability.
Why it matters
Non-engineer analysts can now author and deploy routine automations, while engineers can build sophisticated enrichments with familiar tooling. Versioned automation reduces risk when changing active detection rules.
Practical tip
Start by authoring low-risk enrichment rules (labeling, metadata tagging) to validate behavior, then gradually add automated responses. Keep test datasets for validating script changes before enabling in production.
Security, Privacy, and Compliance
What changed
- Role-based access control (RBAC) with finer-grained permissions and attribute-based policies.
- Immutable audit logs that record actions, queries, and rule changes with tamper-evidence.
- Data masking and field-level redaction for sensitive attributes during search and export.
- Improved secrets management and rotation support for connector credentials.
Why it matters
Organizations can enforce least-privilege access, demonstrate chain-of-custody, and meet stricter compliance demands. Masking reduces leakage risk during collaborative investigations.
Practical tip
Enable field-level redaction for personally identifiable information (PII) fields in shared workspaces. Configure audit log exports to your SIEM for long-term retention and compliance reporting.
Model Explainability and Diagnostics
What changed
- A relevance diagnostics panel that explains why a result was ranked highly, showing contributing signals and scores.
- Query performance analytics and slow-query tracing with actionable tuning suggestions.
- Drift detection for semantic models with periodic automated sampling and alerts when performance degrades.
Why it matters
Analysts and administrators gain visibility into model behavior and can justify search outcomes for audit purposes. Drift detection prevents silent degradations in semantic relevance.
Practical tip
Use the diagnostics panel during onboarding to demonstrate how query rewrites or filters affect results. Schedule automatic drift alerts to trigger re-training or review.
Scalability, HA, and Deployment
What changed
- Improved horizontal scaling patterns for both indexing and query services.
- Containerized reference deployment with Helm charts and Kubernetes-ready manifests.
- Blue/green deployment support and rolling upgrades with zero-downtime migration for many components.
Why it matters
Enterprises can scale ASI predictably and perform safer upgrades with minimal interruption to investigators.
Practical tip
Adopt the Helm chart defaults for initial deployments, then tune replica counts and resource requests based on observed CPU and memory metrics. Use blue/green for major upgrades to minimize investigative disruption.
Migration and Backwards Compatibility
What changed
- Migration tools that automatically convert common configuration and connector definitions from 2.x to 2.5 formats.
- Compatibility shim for older integrations that cannot be updated immediately.
- A deprecation timeline and compatibility matrix provided in the upgrade guide.
Why it matters
Upgrading is less risky and requires fewer manual changes, reducing friction for teams that depend on existing pipelines.
Practical tip
Run the migration tool in a staging environment first. Review the compatibility matrix for any deprecated APIs and plan for replacement if your environment relies on them.
Observability and Monitoring
What changed
- Improved metrics emitter and dashboards for ingestion, query latency, cache hit rates, rule execution success, and connector health.
- Alert templates for common failure modes (connector failures, ingestion lag, rule errors).
- Tracing integration for end-to-end request visibility.
Why it matters
Faster detection of operational issues reduces downtime and improves reliability for investigation teams.
Practical tip
Import the provided dashboard templates into your monitoring stack and set sensible alert thresholds based on your baseline usage.
Documentation, Training, and Support
What changed
- Expanded documentation with migration walkthroughs, best-practice guides, and a cookbook of common investigative patterns.
- New interactive tutorials in the sandbox that guide users through triage, automation, and enrichment tasks.
- Enhanced enterprise support tiers with faster SLAs for critical incidents.
Why it matters
Teams can onboard faster and resolve issues more quickly with practical, scenario-based guidance.
Practical tip
Run the interactive sandbox tutorial as part of any internal training session so analysts can learn new features hands-on.
Limitations and Considerations
- Semantic ranking improvements work best when there’s a reasonably sized labeled or interaction dataset; small teams may see smaller gains.
- Some new features (advanced diagnostics, drift detection) require additional resource allocation and configuration.
- Custom connector maintenance still requires monitoring when upstream APIs change—connectors simplify development but don’t eliminate operational work.
Conclusion
AutomaticSearch Investigator 2.5 is a focused, practical upgrade designed to improve the core investigator experience: finding higher-quality results faster, automating repetitive tasks safely, integrating broader data sources, and strengthening security and compliance. The release emphasizes operational maturity (scalability, monitoring, migration tooling) so teams can adopt these improvements without major disruption.
To get started, follow this checklist:
- Run the migration tool in staging.
- Enable relevance diagnostics and review results for a handful of representative queries.
- Import dashboard and alert templates.
- Trial the new connector SDK in sandbox.
- Create shared workspace templates for your top 3 case types.
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