AI Security & Secure AI Product Development
Build and integrate AI capabilities with security considered from architecture through launch, grounded in the realities of product development.
Discuss an engagementSecurity for systems that behave differently.
LLM applications and agents change how data, tools, and trust boundaries interact. We help teams reason through those changes and make practical design choices.
Secure AI integration
Assess how AI features connect to existing products, data, identity, and operational controls.
LLM, RAG, agents, and MCP
Review retrieval, tool use, agent behavior, and Model Context Protocol integrations for relevant trust and abuse paths.
AI system architecture
Design boundaries, permissions, observability, and controls that match the product and its risk.
Threat modeling and adversarial testing
Examine misuse paths and test important assumptions in the context of the actual application.
AI-assisted development security
Help engineering teams apply appropriate review and safeguards when using AI to write or change code.
Development guidance
Give product and engineering teams security input during design and implementation, not only at the end.
Work through the real system.
AI Security is most useful when it is connected to a concrete product, workflow, and set of users. We start with architecture and intended behavior, then identify where untrusted inputs, data access, or tool permissions matter.
Ascensis has delivered Secure AI Product Development consulting for a fintech company. We can discuss the approach and relevant experience without disclosing client details.
Work directly with founder Chris Martorella. Engagements are shaped around the decisions, people, and delivery cadence of your organization.