AVP, Data Security

## AVP, Data Security Apply locations:
Washington, DCtime type:
Full time posted on:
Posted 7 Days Agojob requisition :
R-00104

The Associate Vice President of Data Security provides strategic leadership and direction for the enterprise data security program, ensuring the confidentiality, integrity, and availability of sensitive data across the organization. This role leads the design, execution, and continuous improvement of data security initiatives that align with business objectives, regulatory requirements, and evolving threat landscapes.
The AVP of Data Security oversees complex, multi-year security programs and high-impact projects, coordinating cross-functional teams across technology, engineering, legal, compliance, and business units. Through strong program and project management discipline, the role drives measurable risk reduction, ensures timely delivery of security initiatives, and maintains accountability for outcomes.
As a senior leader, this position bridges technical data security capabilities with organizational priorities, translating risk and security requirements into actionable strategies for executive stakeholders. The role is responsible for guiding the adoption and optimization of data security technologies—including data classification, encryption, data loss prevention (DLP), identity and access controls, and monitoring solutions—while establishing governance, metrics, and reporting to assess program effectiveness and maturity.
In-Office Requirement: 4 days per week Primary Responsibilities
* Lead the strategy, implementation, and optimization of enterprise DLP capabilities to prevent unauthorized disclosure of sensitive data, including PII, PCI, MNPI, and proprietary investment information.
* Oversee content inspection technologies leveraging pattern matching (e.g., SSNs, account numbers), keyword analysis, and checksum validation.
* Guide adoption of AI/ML-based DLP techniques that incorporate user behavior analytics and contextual risk to detect anomalous data activity.
* Ensure coverage for data at rest, in motion, and in use across endpoints, email, cloud collaboration platforms, and SaaS applications.
* Establish and mature DSPM capabilities to continuously discover, classify, and assess risk across enterprise data stores, including cloud platforms, data warehouses, and investment systems.
* Drive risk-based prioritization of data exposures caused by misconfigurations, excessive permissions, and insecure data flows.
* Integrate DSPM insights with DLP, IAM, encryption, and cloud security controls to create a unified data protection posture.
* Define metrics, reporting, and executive dashboards to communicate data risk and posture trends to senior leadership

Education & Certificates
* Bachelor’s degree, required
* Master degree in a related technical field or finance, preferred
* CISSP, CISM, or other vendor agnostic security certifications
* Microsoft Purview Data Loss Prevention experience required

Professional Experience
* Minimum of 6+ years of overall relevant technical experience, required
* Enterprise Data Loss Prevention (DLP) Architecture and Implementation
* Data Security Posture Management (DSPM) and Data Discovery
* Data Protection Technologies and Controls
* Advanced Data Monitoring and Analytics:
Familiarity with content inspection techniques (pattern matching, checksum validation, keyword analysis) and AI/ML-driven analytics, including user behavior analytics (UBA/UEBA), to detect anomalous data access and potential exfiltration events.
* Security Program Integration and Metrics Development:
Ability to integrate DLP, DSPM, IAM, encryption, and cloud security controls into a cohesive data protection architecture while establishing measurable security metrics, reporting frameworks, and executive dashboards to track program maturity and risk reduction.

Competencies & Attributes
* Strong understanding of Data Loss Prevention (DLP) concepts, including content inspection, pattern matching (PII, PCI, PHI), and policy-based data protection
* Experience with AI/ML-driven data risk detection, leveraging behavioral analytics and contextual analysis to identify anomalous data activity
*…