AI Security Analyst

About AlphaSense:  The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! About the Role We are building an AI Security and Governance capability and need an AI Security Analyst to be the front line for detecting, investigating, and containing risk across every AI tool, agent, and model touching AlphaSense's environment.  You will monitor enterprise AI usage end to end, hunt for unauthorized ("shadow") AI and rogue agent activity, and turn raw AI telemetry into triaged findings the security and governance team can act on. Working alongside the Automation Engineer, Data Analyst, and Director, you will be a primary contributor to the evidence base underpinning our ISO 42001 certification and our broader AI risk posture. Key Responsibilities AI Tool Discovery & Shadow AI Monitoring Continuously monitor CASB/SWG, OAuth, and endpoint telemetry to discover unsanctioned AI tools, browser extensions, and API-level agents in use across the business. Classify findings by risk tier and escalate unauthorized deployments for containment. Rogue Agent Detection & Investigation Investigate alerts related to autonomous agents and AI-powered workflows (e.g., anomalous tool chains, unexpected data access, credential misuse) including threats such as OpenClaw and similar agent-based attack patterns. Document findings and drive remediation with the Automation Engineer and Identity teams. AI Data Exfiltration Detection & DLP Monitor DLP and AI usage logs for signs of sensitive data leaving the environment through AI tools (prompts, uploads, plugins, MCP connectors). Tune detection rules and validate that AI-specific DLP controls are operating as designed. Developer AI Tool Governance Monitor usage of developer-facing AI tools (GitHub Copilot, Claude Code, Codex, and similar) for policy compliance, credential exposure, and non-human identity risk. Partner with Engineering to ensure code-assist and agentic dev tools operate within approved guardrails. AI Logging, Retention & Audit Support Maintain and validate AI activity logging and data retention controls to ensure auditability. Assemble and organize evidence supporting ISO 42001 audits, EU AI Act readiness, and internal AI impact assessments. Detection Tuning & Continuous Improvement Analyze false positive/negative trends across AI security detections and recommend threshold and logic adjustments. Feed recurring patterns into the Automation Engineer's playbook backlog. Cross-functional Collaboration Partner with Security Operations, Identity, Legal, and AI/ML Engineering to align findings with existing incident response processes, and support the Director in preparing risk findings for governance council and executive reporting. Who You Are Basic Requirements  3-5+ years in security analysis, SOC, or GRC analyst roles  Working knowledge of SIEM platforms (Splunk, Microsoft Sentinel, Google Chronicle) and DLP/CASB tooling  Understanding of AI/LLM risk concepts: prompt injection, data exfiltration via AI tools, model/agent misuse, shadow AI  Familiarity with non-human identity concepts (service accounts, API keys, OAuth tokens) and their security challenges  Understanding of threat detection logic (MITRE ATT&CK; exposure to MITRE ATLAS or OWASP LLM Top 10 a plus)  Comfortable reading and interpreting logs, API telemetry, and structured/unstructured investigation data Basic scripting or query proficiency (Python, SQL, or SPL/KQL) for investigation and reporting  Strong written documentation habits Ability to translate technical findings into clear, risk-based narratives Familiarity with cloud environments (AWS, Azure, or GCP) Nice to Have   Exposure to AI governance frameworks (ISO 42001, NIST AI RMF, EU AI Act risk tiers)  Experience supporting audit evidence collection or control testing (SOC 2, ISO, or simi