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Datadog is seeking a Staff-level Software Engineer for their Security Research team in New York. This role involves leading the development of internal “Security Research Experience” services and infrastructure, tackling challenges such as:
- Scaling threat hunting automation across millions of events per minute
- Architecting threat intelligence delivery for Datadog Security customers
- Exploring AI security, cloud security, and threat detection use cases
Apply here: Staff Software Engineer, Security Research | Datadog Careers
You Should Know:
1. Threat Hunting Automation at Scale
To analyze millions of security events efficiently, engineers use:
– ELK Stack (Elasticsearch, Logstash, Kibana) for log analysis
– Apache Kafka for real-time event streaming
– Python/Pandas for large-scale data processing
Example Command (Log Analysis with ELK):
Query Elasticsearch for suspicious login attempts
curl -XGET 'http://localhost:9200/logs-/_search?pretty' -H 'Content-Type: application/json' -d'
{
"query": {
"bool": {
"must": [
{ "match": { "event.type": "authentication_failure" } },
{ "range": { "@timestamp": { "gte": "now-1d/d" } } }
]
}
}
}'
2. Threat Intelligence Delivery
To automate threat intelligence, engineers may use:
- YARA rules for malware detection
- Sigma rules for SIEM detection
- STIX/TAXII for threat intelligence sharing
Example YARA Rule:
rule Detect_Malicious_PowerShell {
meta:
description = "Detects suspicious PowerShell commands"
author = "Security Team"
strings:
$s1 = "Invoke-Expression" nocase
$s2 = "DownloadString" nocase
condition:
any of them
}
3. AI Security & Cloud Threat Detection
Emerging AI security tools include:
- Falco for cloud runtime security
- TensorFlow Privacy for ML model security
- AWS GuardDuty for cloud threat detection
Example Falco Command:
Monitor for unexpected container privilege escalation falco -r /etc/falco/falco_rules.yaml -e "container and proc.name=bash and evt.type=execve"
What Undercode Say:
Security research engineering is evolving with AI-driven threat detection and automated response systems. Companies like Datadog are pushing for scalable security solutions, integrating real-time analytics and cloud-native protections. Expect more ML-based anomaly detection and automated remediation scripts in the future.
Expected Output:
- Security event analysis at scale using ELK & Kafka
- Threat intelligence automation via YARA/Sigma
- AI-powered cloud security with Falco & GuardDuty
Prediction:
AI-driven security tools will dominate threat detection by 2025, reducing manual SOC workloads by 40%. Cloud-native attacks will rise, requiring automated runtime protection solutions.
Relevant URLs:
References:
Reported By: Zack Allen – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
