Funding Round: Realm Security's AI Pipeline Lands $15M to Cut SIEM Costs
October 9, 2025 - 10:18 am IDT
Realm Security raised $15M in Series A funding to scale its AI-native security data pipeline. The round was led by Jump Capital with participation from Glasswing Ventures and Accomplice VC.
The pitch is simple: send only relevant, structured telemetry to your SIEM and cut spend. For product teams, this is a control layer for cost, data quality, and signal fidelity.
The problem product teams are facing
SOCs are drowning in raw logs while SIEM costs climb with every extra gigabyte. A SANS SOC survey reports that 42% of teams push all data into SIEM without a real strategy, which slows investigations and inflates budgets.
Legacy pipelines act like pass-throughs. They forward noise, leave enrichment to downstream tools, and lock teams into rigid vendor workflows.
What Realm Security is building
The platform acts as an AI-driven triage layer that analyzes, filters, normalizes, and enriches data before it hits a SIEM or data lake. The result: cleaner inputs, fewer false positives, and clearer signals for detection and response.
It provides a single point of ingestion to route, suppress, and enrich events across sources, helping teams avoid vendor lock-in. CEO Pete Martin positions it as a root-level fix for data sprawl and SIEM spend.
Why product development should care
- Cost control lever: Reduce ingestion volume without losing detection coverage.
- Higher-quality signals: Standardized schemas and pre-enrichment improve rule accuracy and model performance.
- Faster iteration: A central policy layer makes it easier to test, route, and promote detection changes.
- Vendor agility: Decouple producers and consumers to switch SIEMs or add data lakes without rework.
- Operational clarity: Observability on what's sent, dropped, or transformed becomes a product metric, not guesswork.
What to validate in a proof-of-value
- Data reduction rate by source and event type (target thresholds for 30/60/90 days).
- Detection impact: Precision/recall changes on priority detections and MTTR on real incidents.
- Schema discipline: Out-of-the-box mappings, custom fields, versioning, and drift alerts.
- Policy-as-code: Review, test, and rollout workflows with rollback.
- Privacy and compliance: PII redaction, tokenization, lineage, and audit trails.
- Resilience: Backpressure handling, retries, dead-letter queues, and fail-open/closed options.
- Cost model: Transparent pricing that maps to your actual volume and routes.
Integration questions to ask
- Connectors: Coverage for your top log sources, EDR, cloud trails, identity, and SaaS apps.
- Destinations: Compatibility with your SIEM(s), data lake, and alerting stack.
- Real-time vs. batch: End-to-end latency under load, including enrichment calls.
- Enrichment: Built-in IP/geo, identity context, asset tags; custom lookups and caching.
- Governance: Role-based policies, per-tenant isolation, and environment separation.
- Ops model: SLAs, on-call escalation, and runbooks for noisy source spikes.
KPIs to track post-deployment
- SIEM ingestion volume and cost per day/week by source.
- Alert quality: False positive rate and analyst time per alert.
- Mean time to detection and investigation.
- Schema drift events and policy change lead time.
- Coverage: Percentage of priority sources routed with enrichment policies.
Why the funding matters
$15M gives the company room to expand connectors, policy tooling, and partnerships with SIEM and cloud vendors. For buyers, that often translates into faster roadmap delivery and stronger enterprise support.
Context for security data strategy
Modern SIEM strategies benefit from pre-ingestion normalization and trimming noisy sources. If your team still dumps everything into the SIEM, consider a staged rollout: start with high-volume logs (e.g., VPC flow, DNS), apply suppression and enrichment, and measure cost and detection changes before expanding.
For background on log management principles, see NIST SP 800-92: Guide to Computer Security Log Management here.
Next steps for product teams
- Run a 60-90 day pilot with a clear baseline and target metrics.
- Codify policies in version control and integrate with CI for validation.
- Partner with SecOps to define keep/drop rules tied to detection objectives, not gut feel.
- Build a dashboard that exposes cost and signal health to stakeholders.
If your team needs to upskill on AI systems and data pipelines, explore focused learning paths for product roles here.
