AI security assessments.
In minutes.
Turn your AI, RAG or agentic system description into a structured security assessment pack — go-live blockers, risk register, controls, and remediation backlog.
Designed for modern AI delivery stacks
Drel helps security and product teams review GenAI, RAG and agentic systems built with the model providers, cloud platforms and engineering tools they already use.
Built for every AI system type
AI teams ship faster than security can assess.
AppSec and security architecture teams are not staffed to manually threat-model every AI, RAG, or agentic system. Traditional threat modeling tools were not designed for LLM trust boundaries, retrieval authorization, or agentic tool use. Assessments pile up. Systems go live without proper security sign-off.
The solution
Describe your AI system. Answer 8 questions. Get a structured AI Security Assessment Pack in minutes — specific enough for a security architect to use in a real assessment.
Not generic AppSec
Built specifically for RAG pipelines, agentic workflows, and LLM-powered features. Not a cheaper IriusRisk. Not a chatbot with a UI. AI-native threat modeling from the ground up.
A real security assessment. Not a report template.
Every output is specific to your architecture — named blockers with validation tests, threats mapped to OWASP and MITRE, controls with implementation guidance, and a pre-answered enterprise security questionnaire.
Tailored to your AI architecture
Each system type has its own threat model, risk patterns, and control library — built from the specific trust boundaries of that architecture.
Retrieval-Augmented Generation over enterprise knowledge
Employee-facing assistants over SharePoint, Confluence, and ServiceNow introduce unique trust boundaries — retrieval authorization, prompt injection via documents, and identity propagation across the retrieval chain.
Agentic systems with write access and tool execution
LLM agents with API access to GitHub, Slack, Jira, and PagerDuty require explicit policy gates, approval boundaries, and action authorization. Without them, a single injected instruction can trigger cascading write actions.
Customer-facing AI in multi-tenant SaaS products
AI features embedded in B2B SaaS products must enforce strict tenant isolation, prevent cross-tenant data leakage, and produce go-live evidence for enterprise security questionnaires.
The standards your team already uses.
Every threat, control, and remediation item maps directly — so the output lands in your assessment without translation.
Ready to assess your AI system?
No signup required for the demo. Generate your first AI Security Assessment Pack in minutes.