Why 70% of SIEM Projects Fail (And How to Avoid the Same Fate with Elastic 93) + Video

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Introduction:

Security Information and Event Management (SIEM) re-internalization is becoming a critical strategy for organizations seeking to regain control over their security data amidst escalating threats and stringent regulatory demands. However, the reality is that SIEM projects are notoriously difficult to execute, with a high failure rate stemming from misaligned Security Operations Center (SOC) requirements, underestimated costs, and underutilized data, often reducing these systems to expensive log repositories rather than active security tools.

Learning Objectives:

  • Understand the common pitfalls leading to SIEM project failure and how to avoid them.
  • Learn to leverage Elastic 9.3’s features for effective data normalization, detection, and automation.
  • Implement a measurable and actionable Threat Intelligence strategy within your SOC beyond simple Indicator of Compromise (IOC) matching.

You Should Know:

1. Conducting a SOC-Centric Needs Analysis

The primary reason SIEM projects fail is a disconnect between the technology being deployed and the actual operational needs of the SOC. Before migrating or re-internalizing your SIEM, you must perform a thorough analysis of your SOC’s workflows, detection gaps, and response capabilities.

Step‑by‑step guide:

  1. Interview SOC Analysts: Conduct workshops to identify the top three pain points in current investigations, such as data latency, poor search performance, or lack of context.
  2. Map Data Sources: Create a comprehensive inventory of log sources, categorizing them by criticality (e.g., cloud, endpoints, network, identity). Prioritize ingesting high-fidelity, high-value logs over “noisy” data that offers little analytic value.
  3. Define Use Cases: Document specific detection use cases. For example, “Detect anomalous lateral movement using Windows Event ID 4624” or “Alert on AWS Console logins without MFA.” This list will directly inform your SIEM’s correlation rules and data parsing requirements.

2. Implementing Elastic Stack for SIEM Re-internalization

Elastic, with its recent 9.3 release, provides a powerful, scalable platform for re-internalizing your SIEM. The focus should be on using its native capabilities to avoid the pitfalls of complex, custom integrations.

Step‑by‑step guide for Linux (Ubuntu/Debian):

  1. Install Elastic Stack: Add the Elastic GPG key and repository. 
    wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -
sudo apt-get install apt-transport-https
echo "deb https://artifacts.elastic.co/packages/8.x/apt stable main" | sudo tee /etc/apt/sources.list.d/elastic-8.x.list
sudo apt-get update && sudo apt-get install elasticsearch kibana logstash
  2. Configure Elasticsearch: Set cluster name and network host in /etc/elasticsearch/elasticsearch.yml. 
    cluster.name: siem-internal
network.host: 0.0.0.0
discovery.type: single-node
  3. Deploy Elastic Agent: Use the Elastic Agent to centralize configuration management. Run the following on a server to install and enroll it with your Fleet Server. 
    curl -L -O https://artifacts.elastic.co/downloads/elastic-agent/elastic-agent-8.x-linux-x86_64.tar.gz
tar xzf elastic-agent-8.x-linux-x86_64.tar.gz
cd elastic-agent-8.x-linux-x86_64
sudo ./elastic-agent install --url=https://your-fleet-server:8220 --enrollment-token=<your-token>

    This replaces legacy Beats configurations, simplifying the management of log collection, endpoint security, and SIEM data shipping through a single agent.

  4. Mastering Data Normalization with Elastic Common Schema (ECS)

A major source of inefficiency is the lack of data normalization. Ingesting logs in raw, proprietary formats makes correlation and detection difficult. Elastic Common Schema (ECS) provides a standardized way to structure data, ensuring that fields like source.ip, user.name, and `event.action` are consistent across all data sources.

Step‑by‑step guide using Logstash:

1. Create a Logstash Pipeline: Create `/etc/logstash/conf.d/siem-pipeline.conf`.

  1. Process Windows Event Logs: Use the `winlogbeat` input and mutate events to align with ECS. 
    input { beats { port => 5044 } }
filter {
if [bash][provider] == "Microsoft-Windows-Security-Auditing" {
mutate {
rename => { "winlog.event_id" => "event.code" }
copy => { "host.name" => "host.hostname" }
add_field => { "[bash][category]" => "authentication" }
}
if [bash][event_id] == 4624 {
mutate { add_field => { "[bash][type]" => "success" } }
}
}
}
output { elasticsearch { hosts => ["localhost:9200"] } }
  2. Verify Normalization: In Kibana, use the Discover tool to search for event.category:authentication. Confirm that all related login events, regardless of source (Windows, Linux, firewall), now share consistent field names, enabling unified queries.

4. Effective Detection Engineering with Elastic 9.3

Elastic 9.3 introduces enhancements to rule creation and automation. Moving beyond simple IOCs, you should build detection rules based on behavior and sequences of events, a technique often called “correlation.” This reduces false positives and uncovers sophisticated attacks.

Step‑by‑step guide:

  1. Navigate to SIEM Rules: In Kibana, go to Security → Alerts → Rules.
  2. Create a Custom Rule: Click “Create new rule.” Choose “Custom Query” for simple detection or “Threshold” for detecting spikes in activity.
  3. Write a KQL Query for Lateral Movement: Use a sequence detection rule to find a failed login (Event ID 4625) followed by a successful login (Event ID 4624) from the same source IP within 10 minutes. 
    event.category:authentication AND (event.code:4625 OR event.code:4624)

    In the rule’s advanced settings, configure a “Sequence” condition to correlate these events over time.

  4. Configure Response Actions: Leverage the new automation features. In the rule’s “Actions” tab, configure a webhook to trigger a playbook in a SOAR platform or execute a script on an endpoint to isolate the host automatically upon rule match. This moves the SIEM from a detection engine to an active response orchestrator.

5. Integrating Actionable Threat Intelligence

Many organizations simply feed IOCs into their SIEM, resulting in high-volume, low-fidelity alerts. The goal is to integrate threat intelligence to provide context and enrich alerts, allowing analysts to prioritize critical threats.

Step‑by‑step guide:

  1. Set Up Threat Intelligence Feeds: In Kibana, go to Security → Overview → Threat Intelligence. Connect to a MISP instance, an OpenCTI platform, or upload a STIX/TAXII feed.
  2. Enable Indicator Indexing: Configure the system to index indicators, mapping them to ECS fields like `threat.indicator.file.hash.md5` or threat.indicator.ip.
  3. Create Enrichment Rules: Instead of creating an alert every time an IOC is seen, create a rule that enriches the alert with threat intelligence.
    For example, when a process execution event (event.code:1) has a hash that matches a known malware indicator, the rule can add a `threat.tactic.name` and `threat.software` label to the alert.
    This transforms the alert from “Hash detected” to “Potential ransomware activity linked to LockBit group,” providing SOC analysts with immediate, actionable context without chasing false positives from outdated IOC lists.

6. Automating SOC Workflows

A successful SIEM is an “exploited” one, meaning it is actively used and automated. By using Elastic’s integrations and scripting, you can automate repetitive tasks, reducing mean time to respond (MTTR) and analyst burnout.

Step‑by‑step guide using Python and Elastic API:

  1. Generate API Key: In Kibana, go to Stack Management → API Keys and create a key with `manage` and `write` privileges.
  2. Automated IOC Enrichment Script: Write a Python script to pull alerts with specific tags and enrich them with external sources like VirusTotal. 
    import requests
from elasticsearch import Elasticsearch</li>
</ol>

es = Elasticsearch("https://localhost:9200", api_key="your-api-key")

Query for alerts with file hashes that haven't been enriched yet
query = {
"query": {
"bool": {
"must": [
{ "exists": { "field": "file.hash.md5" } },
{ "bool": { "must_not": { "exists": { "field": "threat.enrichment.vt" } } } }
]
}
}
}

alerts = es.search(index=".alerts-security.alerts-", body=query)

for hit in alerts['hits']['hits']:
md5 = hit['_source']['file']['hash']['md5']
vt_url = f"https://www.virustotal.com/api/v3/files/{md5}"
 ... call VT API, parse response, and use es.update() to add the VT data to the alert.

    3. Schedule the Script: Deploy this script as a scheduled task (Cron on Linux, Task Scheduler on Windows) to run every 15 minutes, ensuring all new alerts are automatically enriched with up-to-date threat intelligence.

    What Undercode Say:

    • Re-internalization requires operational discipline, not just technology. Moving back in-house fails without a clear understanding of SOC workflows and a phased approach to data ingestion.
    • Elastic 9.3 provides a unified platform to bridge detection, investigation, and automation. Its agent-based approach and ECS schema are critical for overcoming the “costly log base” syndrome by making data usable and actionable.

    The key to a successful SIEM project lies in shifting focus from log collection to detection engineering and measurable security outcomes. By leveraging Elastic 9.3’s advanced capabilities, organizations can avoid the common pitfalls of poor planning and underutilization. The future of SIEM is not about managing data volume, but about deriving intelligence from data to automate and accelerate the entire security operations lifecycle.

    Prediction:

    As organizations face tightening budgets and increasing data sovereignty regulations, the trend of SIEM re-internalization will accelerate, moving away from legacy “ingest-all” models. Success will be defined by the adoption of open, scalable architectures like the Elastic Stack, where the primary value is derived from automated response playbooks and integrated threat intelligence, effectively commoditizing log storage and shifting vendor competition to detection efficacy and operational analytics.

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