Principal AI Threat Detection Engineer - Insider Threat

Blackbaud

3d•$117,200 - $157,500•Remote

About The Position

We are looking for a savvy, high-performing Principal AI Threat Detection Engineer – Insider Threat to lead the strategy, design, and day-to-day management of Blackbaud’s Insider Threat program as it matures into an AI-augmented capability, protecting Blackbaud’s and our clients’ information. As a technical leader within Security Engineering, this individual will direct AI-driven and agentic tooling to investigate anomalous events and alerts, detect malicious and anomalous insider activity, and reverse engineer malware, while personally applying the contextual judgment, escalation authority, and accountability that AI cannot provide on its own. The Principal AI Threat Detection Engineer serves as the subject matter expert for Insider Threat tooling, User and Entity Behavior Analytics (UEBA), Security Orchestration Automation and Response (SOAR) platforms, and the AI/ML models underpinning them, validating model outputs, tuning detection logic, and partnering with leadership to report on the business impact of theft, destruction, alteration, or denial of access to information. As the program shifts more first-pass analysis to AI, this role increasingly focuses human effort where it matters most: resolving ambiguous or high-stakes cases, overseeing AI-assisted decisions, and troubleshooting complex threats that impact the information security infrastructure at the data, application, service, operating system, and network levels.

Requirements

8+ years of Security Engineering and Analysis experience, preferably in Threat Detection and Response, including demonstrated experience supporting Insider Threat programs
5+ years of IT or networking experience
Hands-on experience administering, tuning, and optimizing Insider Threat detection tools, User and Entity Behavior Analytics (UEBA) platforms, and the AI/ML models that power them
Experience with SOAR tools and playbook development, including explicit expertise applying AI-driven and agentic automation to security operations and critically evaluating AI-generated findings for accuracy
Experience with security metrics and reporting, preferably including automating recurring metrics and reporting processes
Demonstrated ability to critically evaluate AI/ML model outputs
Intermediate to Advanced Linux/Unix OS and Windows knowledge
Firewall rule and policy, and network routing fundamentals
Ability to manage parallel tasks and accurately document resolutions
Proven ability to implement automation through scripting (e.g., PowerShell, PERL, Python, Bash scripting), including integrating AI/ML APIs or agentic frameworks into detection workflows
Experience leveraging APIs to integrate third-party tooling into an existing tool stack
Familiarity with cyber security frameworks such as NIST and MITRE ATT&CK, and awareness of emerging AI governance and security frameworks (e.g., NIST AI RMF, MITRE ATLAS)
Industry-recognized professional certification such as Security+, CBROPS, CSA, CEH, GSEC, SSCP

Nice To Haves

CISSP, GBFA, GCDA, GCIA, GCIH, GMON, GNFA, GOSI, GPEN, GPPA, GREM, GSOC, OSDA, OSCP
Working knowledge of network packet analysis tools
Direct experience with malware and analysis techniques and methodologies
Prior experience leading or mentoring a security engineering or analyst team through periods of technology and process change, including adoption of AI-driven tooling
Experience presenting insider risk metrics and program updates to senior leadership
Experience partnering with data science or AI/ML engineering teams to build, validate, or govern detection models

Responsibilities

Lead the maturation of Blackbaud’s Insider Threat detection program toward an AI-augmented capability, including administration, tuning, and optimization of Insider Threat tools, UEBA platforms, and the AI/ML models underneath them
Perform intrusion and insider risk analysis using SIEM technology, UEBA behavioral analytics, AI-generated insights, reports, data visualization, log analysis, and pattern analysis, applying human judgment to validate AI findings and separate true signal from noise
Direct AI-driven hunting tools and agents to identify threat actor groups (external and insider) and their respective tactics, techniques, and procedures, personally leading the investigation of cases that require nuanced human judgment
Serve as the human escalation point and first responder for security events that AI and automated tooling flag but cannot fully resolve, via email, phone, and tickets across corporate user networks, data centers, and cloud environments
Own remediation of information security incidents, including insider threat investigations, ensuring AI-assisted findings are verified and contextualized before action is taken
Document and communicate findings, escalate critical incidents, and interact with lines of business, HR, Legal, and other stakeholders on sensitive insider matters, exercising the discretion and judgment that AI systems cannot provide
Design and build AI-driven and SOAR-based automated workflows within Detection Engineering, defining the guardrails, escalation thresholds, and human checkpoints that keep automation safe, effective, and improve analyst performance
Champion responsible use of AI-driven analysis and automation to enhance detection accuracy and accelerate triage, while evaluating model accuracy, bias, and drift to keep detection logic explainable and trustworthy
Document automation and AI model deployment processes, to include defining pre-build requirements, validation criteria, and human review checkpoints for high-risk decisions
Utilize AI and automation to build metrics and dashboards supporting the Insider Threat and broader detection program, applying human interpretation to translate outputs into decisions leadership can act on
Serve as a thought leader on the evolving division of labor between AI and human analysts – determining which decisions should be automated, which require human review, and how that boundary should shift as the program matures – as well as on new alert content, data correlation, and anomaly thresholds
Improve and challenge existing processes and procedures in a very agile and fast-paced cyber security environment
Keep current on the threat landscape, insider risk trends, cyber security developments, and emerging AI/ML techniques relevant to detection engineering
Adapt to fluid infrastructures, and evaluate, pilot, and integrate new AI-enabled technologies and platforms
Act as peer reviewer and technical escalation point within the core security engineering team, including reviewing AI-assisted detection logic and automation before deployment
Advise and inform leadership on how to optimize the current toolset and evaluate future tools, including UEBA, SOAR, and AI-enabled analysis platforms, recommending where automation should expand and where human oversight must remain