AI Product Security & SaaS Security Architecture
AI product security for SaaS teams shipping agentic, RAG, and LLM-enabled products.
David translates AI risk, data governance, product security, and security architecture into practical advisory work, scorecards, and evidence-backed programs.
What you walk away with
Relevant Experience
Experience includes Splunk, Forescout, Devo, Cornerstone, Unum, Disney, defense, and enterprise SaaS work
Who I Work With
Specialist security help for SaaS and AI-native teams
How I Can Help
Hire David for AI product security work that becomes deployable engineering work.
I help B2B SaaS and AI-native companies review high-risk product architecture, test AI systems adversarially, harden agentic workflows, and produce evidence engineering teams can use.
AI Product Security Assessment
Focused review of LLM features, RAG systems, copilots, model integrations, data flows, logging, and customer-facing AI surfaces.
- AI system inventory
- Data-flow review
- RAG authorization
- Remediation backlog
AI Red Team & Adversarial Testing
Evidence-driven adversarial testing for LLM features, RAG systems, copilots, agents, and tool-calling workflows.
- Prompt injection tests
- Jailbreak scenarios
- Tool abuse paths
- Retest guidance
Agentic Workflow Hardening
Secure delegated AI workflows before they can query data, call tools, update records, or trigger production-side effects.
- Tool permissions
- Approval boundaries
- Least privilege
- Audit logging
SaaS Product Security Review
Senior architecture review for SaaS products, platforms, APIs, admin surfaces, integrations, tenancy, logging, and abuse paths.
- Trust boundaries
- Authz and tenancy
- API risk review
- Logging gaps
No commitment - 30 min scoping call to understand your stack
Recent articles written
Recent AI security articles from this person
Public-safe editorial writing, technical analysis, and market-intelligence coverage.
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