AI Security Clearance.
Before AI ships.
Drel turns AI system designs into clearance decisions, evidence records, and audit-ready review dossiers — so your security, AI, and governance teams can decide what reaches production.
Works with the stack you already use.
Drel helps security and product teams review AI, RAG and agentic systems built with the model providers, cloud platforms and engineering tools they already use.
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.
Structured clearance, not a generated report.
Describe your AI system. Review the agentic architecture. Get a structured clearance decision backed by evidence grading, required controls, and a sign-off chain — defensible enough for an AI Committee, regulator, or board.
Built for AI, not generic AppSec.
Built specifically for RAG pipelines, agentic workflows, and LLM-powered features. Purpose-built for AI security clearance, not generic AppSec workflows or one-off generated reports.
A clearance decision, not a generated report.
Every output is specific to your AI system — named blockers before production, required controls with owners and deadlines, evidence gaps that must close before go-live, and a sign-off chain that creates an audit trail.
Security reviews that end with a decision.
Drel turns AI system reviews into defensible go-live decisions, linking blockers, evidence, ownership, sign-offs, and review triggers into one audit-ready record.
Clear what can ship
Move from review inputs to a go-live state: proceed, conditional, restricted pilot, hold, or decline.
Expose what blocks release
Separate advisory findings from production blockers, missing gates, and unresolved assumptions.
Grade the evidence
Track whether each claim is explicit, inferred, assumed, missing, or verified.
Leave the audit trail
Capture rationale, owners, sign-offs, versions, and re-review triggers in one defensible record.
Built for every AI system type.
RAG assistants, tool-using agents, customer-facing AI features — each architecture pattern carries distinct trust boundaries, retrieval risks, and control requirements. Drel maps all of them to the blockers, evidence states, and clearance decision they require.
- Internal RAG assistant
- Customer-facing chatbot
- LLM gateway
- Agent with tools
- Agentic automation
- Multi-agent workflow
- B2B SaaS AI feature
- Vendor AI assessment
- Embedded AI capability
Reviewed through one clearance model: blockers, evidence states, control ownership, sign-offs, and re-review triggers.
Every architecture, its own threat model.
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 clear your first AI system?
No signup required for the demo. See what a clearance decision looks like for a real enterprise AI agent.