Senior Security Researcher

What matters most

• Developing High-Signal Detections for Hybrid Attacks

  The core of this role is translating research into actionable security outcomes. You must demonstrate the ability to analyze complex, multi-stage attack chains that traverse cloud services, identity, and AI systems, and then convert those insights into high-fidelity detection logic that minimizes false positives at a global scale.

• Advancing AI Security and Explainability

  You are expected to partner with AI teams to secure AI-driven applications. This requires not just identifying vulnerabilities in AI models or infrastructure, but also improving the explainability of security detections, ensuring that automated defenses are transparent and trustworthy for engineering stakeholders.

• Large-Scale Telemetry Analysis and Automation

  Success depends on your proficiency in query languages like KQL or SQL to perform deep-dive investigations across massive datasets. You must be able to build scalable research prototypes and automation tools that turn raw telemetry into proactive threat hunting capabilities and real-time attack disruption.

Likely challenges to think through

• Balancing Research Depth with Product Scalability

  A common challenge in large-scale security research is creating prototypes that are technically sophisticated but also performant enough to be deployed across Microsoft Defender's global infrastructure. Prepare by discussing how you have previously navigated the trade-off between complex detection logic and the latency or resource constraints of production environments.

• Navigating Cross-Functional Collaboration

  You will need to influence product, engineering, and data science teams who may have competing priorities. Prepare by identifying examples of how you have communicated complex security research to non-security stakeholders to drive adoption of your findings or to secure buy-in for new security features.

Topics to prepare

• Hybrid Cloud and AI Attack Vectors

  Research and map how modern adversaries pivot between cloud infrastructure, identity providers, and AI-driven applications. Exercise: Create a conceptual threat model for an AI-integrated enterprise application, identifying potential lateral movement paths between cloud storage, identity services, and the AI model endpoint.

• MITRE ATT&CK for Cloud and Containers

  Deepen understanding of the MITRE ATT&CK framework specifically applied to cloud-native environments and Kubernetes. Exercise: Select a specific cloud-based adversary technique and map it to the corresponding telemetry logs available in Azure or AWS, identifying the specific indicators of compromise (IOCs) that would trigger a high-signal detection.

Skills to revise

• KQL for Large-Scale Telemetry Analysis

  Develop proficiency in Kusto Query Language (KQL) to query massive datasets for threat hunting. Exercise: Write a KQL query to detect anomalous service principal behavior or unusual API calls within a simulated cloud environment, focusing on minimizing false positives.

• Detection Logic and Automation

  Use Python to build prototypes that automate the detection of malicious behavior. Exercise: Develop a Python script that parses cloud audit logs to identify and alert on unauthorized configuration changes in a containerized environment, demonstrating how to integrate this logic into a broader security pipeline.

• Behavioral Analytics for Threat Hunting

  Apply behavioral analytics to differentiate between legitimate administrative activity and attacker behavior. Exercise: Analyze a set of historical incident data to define a baseline of 'normal' behavior for a cloud service account, then identify deviations that indicate potential credential compromise.

• Map hybrid attack scenarios to MITRE ATT&CK

  Prepare a technical walkthrough of a complex, multi-stage attack you have researched or mitigated that traverses cloud, identity, and endpoint boundaries. Explicitly map the attacker's techniques to the MITRE ATT&CK framework and explain how you identified the pivot points.

• Demonstrate large-scale telemetry analysis

  Prepare examples of how you have used KQL, SQL, or Cypher to hunt for threats within massive datasets. Focus on how you reduced noise to create high-signal detections and how you validated those detections against real-world behavioral patterns.

• Showcase cloud and container security expertise

  Be ready to discuss specific security challenges in Kubernetes and cloud-native services like Key Vault or DNS. Prepare to explain how you have secured these environments against common adversary tooling and how you automate security prototypes to scale across global infrastructure.

• Formulate questions on AI security and collaboration

  Prepare thoughtful questions regarding the team's current approach to AI security, such as how they balance detection explainability with model complexity, and how researchers partner with data science teams to integrate new research into production Defender products.