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Saviynt

Cloud identity security and access governance

AI Detection Engineer - SOC Analyst 4

Security Operations & Incident ResponseConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedHybrid

About the Job

Requirements

  • A bachelor's degree in Computer Science, Information Security, Data Science, or a related field.
  • 8–12 years of experience in cybersecurity, including at least 4 years focused on detection engineering, threat detection, or security analytics.
  • Hands-on experience building detections for at least one major cloud provider (AWS, Azure, or GCP), including native security services and log sources.
  • Practical experience applying machine learning, anomaly detection, or AI-powered tools to security use cases, with understanding of AI/ML model development, tuning, and evaluation.
  • Proven experience creating detection content using SIEM platforms such as Splunk, Azure Sentinel, or Chronicle; EDR solutions such as CrowdStrike or Microsoft Defender; and cloud security tools.
  • Strong programming skills in Python, which is required, with experience in PowerShell or Bash and the ability to build detection pipelines and automation frameworks.
  • Hands-on experience with SOAR platforms, detection-as-code frameworks, log analysis, and data correlation techniques.
  • Expert-level understanding of the MITRE ATT&CK framework and its application to detection engineering and threat modeling.
  • Ability to analyze complex data sets, identify patterns, and translate findings into detection logic.
  • Adhere to Saviynt's information security and privacy policies and procedures, including annual security training.

Responsibilities

  • Design, develop, and deploy advanced detection rules and logic across SIEM, EDR, CSPM, and cloud-native security platforms.
  • Build and maintain detection-as-code using modern frameworks and version control systems such as Git.
  • Create high-fidelity, low-noise detections mapped to the MITRE ATT&CK framework, focusing on cloud-specific threats and techniques.
  • Research emerging threats, tactics, techniques, and procedures, and translate threat intelligence into actionable detection content.
  • Test and validate detection efficacy using purple team exercises and adversary emulation frameworks.
  • Use AI/ML capabilities within security platforms to improve threat detection accuracy and reduce false positives.
  • Build and tune machine learning models for anomaly detection, behavioral analytics, and predictive threat identification.
  • Integrate generative AI and large language models to accelerate alert triage, investigation workflows, and threat analysis.
  • Evaluate and implement AI-powered security tools for automated threat detection, alert enrichment, and investigation assistance.
  • Monitor and optimize AI/ML model performance, addressing data quality, model drift, and false positive and false negative rates.
  • Serve as a subject matter expert for cloud security detection engineering across AWS, Azure, and GCP environments.
  • Design detections using cloud-native logs such as CloudTrail, Azure Activity Logs, and GCP Audit Logs, and security services such as GuardDuty, Security Command Center, and Defender for Cloud.
  • Build detections for cloud-specific threats, including misconfigurations, identity compromise, data exfiltration, and infrastructure attacks.
  • Monitor container and Kubernetes environments and develop detections for runtime threats and supply chain attacks.
  • Design and implement automated detection deployment pipelines using secure CI/CD methodologies.
  • Build custom scripts in Python, PowerShell, or Bash for automated alert enrichment, evidence collection, and response actions.
  • Develop and maintain automated response playbooks in SOAR platforms to handle detection-triggered workflows.
  • Integrate security tools via APIs to create automated detection and response ecosystems.
  • Identify opportunities to apply automation and AI to reduce mean time to detect and mean time to respond.
  • Analyze detection performance metrics, false positive rates, and coverage gaps to drive continuous improvement.
  • Collaborate with threat intelligence, incident response, and threat hunting teams to refine detection strategies.
  • Create and maintain documentation for detection logic, tuning procedures, and operational runbooks.
  • Provide technical guidance on detection engineering best practices and emerging technologies.
  • Stay current with security research, adversary techniques, and AI/ML advancements in cybersecurity.

Desired Qualifications

  • Certifications such as GIAC Certified Detection Analyst (GCDA), GIAC Cyber Threat Intelligence (GCTI), AWS Certified Security Specialty, Azure Security Engineer Associate, or equivalent.
  • Experience with threat intelligence platforms and threat hunting methodologies.
  • Knowledge of adversary emulation tools such as Atomic Red Team and Caldera.
  • Familiarity with data science tools and frameworks such as Jupyter, pandas, and scikit-learn.
  • Contributions to open-source detection content repositories, including Sigma rules and detection rules.

About the Company

Saviynt

Cloud identity security and access governance

Saviynt provides a cloud-based identity security and access governance platform that helps organizations manage digital identities for employees, vendors, and machines. It offers identity governance and administration, third-party access governance, and cloud privileged access management, using automated workflows and identity analytics to reduce risks and maintain audit readiness. The product helps enterprises implement governance from the start, minimize separation-of-duty risks across apps, and stay compliant while lowering costs through a subscription model and scalable deployment across diverse industries.

Company size
1,001-5,000
Company stage
Series B
Total funding
$1.1B
Headquarters
El Segundo, CA
Founded
2010

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