David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · SPLUNK
Splunk
Splunkbase App Certification Program
Turning a sprawling marketplace security problem into a repeatable app certification, review, and trust program.
Led the security architecture, verification, and delivery model behind Splunkbase App Certification, transforming inconsistent security review across Splunk-built and marketplace apps into a scalable operating model with...
Client
Splunk Inc.
Engagement Type
Full-Time (FTE)
Period
2014–2015
Role
Senior Product Security Engineer
Focus Areas
Marketplace App Security, Application Security Review, Product Security Program Design, Secure SDLC
The Research Narrative
Strategic Problem
The portfolio was too large for one-off manual review and too important for purely advisory checks. Early sampling showed inconsistent quality, configuration, metadata, dependency choices, secrets exposure,...
What David Did
David designed a risk-based certification model that blended fast pattern hunting, linting, SAST, DAST, manual review, and exploitability analysis. The model hard-blocked high-confidence,...
What Became Clearer
The program established Splunkbase App Certification as a durable security operating model. More than 800 substantive findings were triaged or addressed, hundreds of apps were brought into...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
Splunkbase was a major extension point for the Splunk platform, with Splunk-built and third-party apps supporting customer use cases across security, observability, IT operations, integrations, and enterprise workflows. As the ecosystem grew, app quality and security became more than an engineering hygiene issue. It became a customer trust issue.
The Challenge
The portfolio was too large for one-off manual review and too important for purely advisory checks. Early sampling showed inconsistent quality, configuration, metadata, dependency choices, secrets exposure, and unsafe execution patterns. The organization needed a review model that could distinguish real release-blocking risk from noise, scale across hundreds of apps, and preserve engineering trust.
What I Did
David designed a risk-based certification model that blended fast pattern hunting, linting, SAST, DAST, manual review, and exploitability analysis. The model hard-blocked high-confidence, high-consequence findings while routing lower-confidence or lower-severity issues into remediation and follow-up. That avoided wasting political capital on noisy findings while still stopping issues that clearly justified a ship decision.
- •Performed early sampling, grep-based analysis, linting, and code scanning to prove the existence of portfolio-level security and quality risk
- •Defined the security and quality bar for Splunkbase App Certification and the Splunk Certified trust signal
- •Designed a risk-based review model that separated hard-blocking findings from warnings, follow-up items, and lower-confidence signals
- •Combined targeted pattern matching, SAST, DAST, manual review, and exploitability analysis rather than depending on a single scanner
- •Prioritized high-consequence and high-confidence issues such as secrets exposure, unsafe execution patterns, vulnerable dependencies, and insecure configuration
- •Created repeatable triage, routing, remediation, and reporting practices across a large app portfolio
- •Built scorecards and progress reporting for weekly, monthly, and quarterly stakeholder cadences
- •Worked with product and engineering teams to make the process predictable enough to survive release pressure
The Outcome
The program established Splunkbase App Certification as a durable security operating model. More than 800 substantive findings were triaged or addressed, hundreds of apps were brought into a structured review process, and the Splunk Certified badge became a clearer customer-facing trust signal. The initiative also strengthened the broader product security evidence story used in enterprise security reviews and deal support.
Research Outcomes
Alert Trust
Reduced noise and improved signal quality for IAM and access-control alerts
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
SIEM Alert Debugging
Noise reduction and signal validation
Dashboard Development
Operational and executive views
Public-Safe Evidence
Shareable insights without sensitive data
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
Executive Reporting
Security data translated for leadership
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Key Deliverables
- •Splunkbase App Certification security criteria
- •Splunk Certified verification and validation requirements
- •Risk-based finding threshold model for hard blocks, warnings, and follow-up items
- •Portfolio security review workflow for Splunk-built and marketplace apps
- •SAST, DAST, grep, linting, and manual review practices tuned for the app ecosystem
- •Finding taxonomy, triage model, and remediation routing process
- •Security scorecards and maturity reporting artifacts
- •Weekly, monthly, and quarterly stakeholder reporting cadence
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.