A SIEM migration is one of the most sensitive changes a security team can make. Logs, alerts, dashboards, and compliance reports are all tightly connected to daily operations. When things work, nobody notices. When they break, everyone does.
We regularly speak with CISOs and SOC leaders who are evaluating a move away from their existing SIEM because it no longer supports how their organisation operates today. The motivation is real. The risk is real too. Migration done without structure often leads to lost data, broken detections, and frustrated analysts.
This blog explains what ArcSight and Elastic SIEM are, why many organisations decide to migrate, and how to follow a structured ArcSight to Elastic SIEM migration checklist that reduces risk and delivers long-term value.
What is ArcSight?
ArcSight is a mature Security Information and Event Management platform widely used in large enterprises. It centralises logs from across the environment, applies correlation rules, and supports compliance reporting for regulated industries.
ArcSight is known for its depth and robustness. At the same time, it often requires specialised skills to manage connectors, correlation logic, and performance tuning. As environments grow and data volumes increase, operational overhead can rise sharply.
What is Elastic SIEM?
Elastic SIEM is part of the Elastic Security offering built on the Elastic Stack. It focuses on fast search, flexible analytics, and modern detection engineering across cloud, hybrid, and on-prem environments. Elastic SIEM appeals to teams that want scalable ingestion, transparent pricing, and the freedom to adapt detection logic as threats and technologies evolve.
Why organisations move from ArcSight to Elastic SIEM
When the SIEM starts demanding more than it gives back. That’s the simple answer.
On the deeper level, migrations usually begin with a quiet frustration. As per our conversations with clients, ArcSight environments become expensive as log volumes grow, forcing teams to limit data ingestion. Operationally, maintaining connectors and correlation rules requires deep platform knowledge that is hard to sustain over time.
Investigations can feel slow, especially when analysts need to pivot across large datasets during active incidents. As organisations adopt cloud platforms and SaaS services, integration gaps become more visible.
Elastic is often seen as a way to regain speed and flexibility. Migration becomes less about replacing a product and more about enabling modern security operations.
Why migration must be done carefully
A SIEM migration touches detection, response, compliance, and reporting. When rushed, it creates risk instead of reducing it.
Common problems include missing log sources, alerts that behave differently overnight, compliance reports that no longer satisfy auditors, and analysts struggling to adapt. These issues usually stem from treating migration as a technical exercise rather than an operational change.
A structured checklist keeps teams focused on intent, not just mechanics.
Read our blog on QRadar to Elastic SIEM checklist and Splunk to Elastic SIEM migration checklist
ArcSight to Elastic SIEM migration checklist
Below is a detailed ten-step checklist designed for enterprise security teams. Each step builds on the previous one and reduces the risk of unpleasant surprises later.
| Step | Focus area | What to do | Why it matters |
| 1 | Define success | Agree on clear goals such as faster investigations or lower cost | Keeps migration aligned with business value |
| 2 | Inventory ArcSight | List all log sources, rules, dashboards, and reports | Reveals what truly needs migrating |
| 3 | Retire dead weight | Identify unused rules and reports | Reduces complexity and noise |
| 4 | Plan architecture | Design Elastic ingestion, storage, and retention | Avoids performance and cost issues later |
| 5 | Choose ingestion method | Decide on Logstash or native integrations | Ensures reliable and flexible data flow |
| 6 | Normalise data | Map logs to Elastic Common Schema | Enables effective correlation and search |
| 7 | Rebuild detections | Translate use cases, not rules | Preserves intent and improves accuracy |
| 8 | Validate with data | Test alerts using real and simulated events | Prevents blind spots at go-live |
| 9 | Run in parallel | Operate ArcSight and Elastic together temporarily | Builds confidence and allows comparison |
| 10 | Train and optimise | Upskill analysts and tune detections | Drives adoption and long-term value |
Step 1: Define what success looks like
Start with outcomes, not tools. Agree on what the migration must achieve. This could be faster investigations, reduced SIEM costs, improved cloud visibility, or better analyst experience. Clear success criteria help prioritise decisions throughout the project. Without them, teams risk rebuilding everything without improving anything.
Step 2: Inventory the ArcSight environment
Document all log sources, connectors, correlation rules, dashboards, and compliance reports. This step often reveals how complex the environment has become over time. It also highlights dependencies that are easy to miss, such as reports tied to audits or alerts relied on by incident response teams.
Step 3: Decide what not to migrate
Not every rule or report deserves a new life. Many ArcSight environments contain legacy detections that rarely fire or dashboards nobody uses.
Retiring this dead weight simplifies migration, reduces noise in the new SIEM, and lowers operational cost from day one.
Step 4: Design the target Elastic architecture
Before ingesting any data, design how Elastic will store, index, and retain logs. Decisions around index structure, access controls, and retention periods affect both performance and cost.
This is also the stage to align architecture with business risk. High-risk data may need longer retention and faster access than low-risk logs.
Step 5: Choose the right ingestion approach
Many organisations use Logstash during transition to ingest data from existing ArcSight feeds or shared log sources. Others move directly to native Elastic integrations. The goal is reliable, consistent ingestion. Whatever approach you choose, it should support validation, scaling, and troubleshooting without disrupting operations.
Step 6: Normalise data using Elastic Common Schema
Field consistency is critical for correlation and search. Mapping logs to Elastic Common Schema ensures detections work across different data sources. Skipping this step leads to fragmented detections, brittle dashboards, and confusion for analysts who expect consistent fields.
Step 7: Rebuild detections based on intent
ArcSight correlation rules rarely translate directly. Instead of copying logic, revisit the purpose of each detection. Ask what behaviour it was meant to identify. Then rebuild it using Elastic queries, thresholds, or analytics. This often results in simpler, more accurate alerts with less noise.
Step 8: Validate with real and simulated data
Testing is not optional. Run detections against historical data and simulate known attack scenarios. Analysts should review alerts for accuracy, context, and actionability. This step prevents blind spots and builds trust in the new SIEM.
Step 9: Run ArcSight and Elastic in parallel
A parallel run allows teams to compare alerts, dashboards, and data completeness. Differences surface early, when they are easier to fix. This approach also gives analysts time to adjust before ArcSight is fully decommissioned.
Step 10: Train analysts and optimise continuously
Migration does not end at go-live. Analysts need hands-on training focused on investigation workflows, not platform features.
Post-migration, tune detections, refine dashboards, and review costs regularly. Elastic rewards continuous improvement, not set-and-forget thinking.
What happens when teams skip the checklist
When steps are rushed or skipped, problems emerge quickly. Data gaps appear because ingestion was not validated. Alerts lose meaning because intent was never reviewed. Compliance reporting becomes a scramble instead of a confidence check.
Most of these issues are avoidable. Structure is the difference between a painful migration and a successful one.
Conclusion
A move from ArcSight to Elastic SIEM can transform how security teams operate. Faster searches, flexible detections, and better visibility are real outcomes. But they only materialise when migration is planned with care.
Our experience shows that organisations who follow a structured ArcSight to Elastic SIEM migration checklist reduce risk, improve analyst adoption, and see value sooner.
At CyberNX, we work alongside your team to plan, execute, and optimise SIEM migrations without disrupting day-to-day operations. Speak to our experts today in case you need managed SIEM services and a clear roadmap forward.
ArcSight to Elastic SIEM migration checklist FAQs
How long does a typical ArcSight to Elastic migration take?
Most enterprise migrations take three to six months, depending on data volume and detection complexity.
Can we keep ArcSight for historical compliance data?
Yes. Many organisations retain ArcSight for legacy data while running Elastic for new detections.
Do we need to migrate every ArcSight rule?
No. Migration is an opportunity to retire low-value detections and rebuild only what matters.
Is Elastic SIEM suitable for regulated industries?
Yes, when compliance reporting and access controls are designed and validated early.
