Security clearance for every AI system
Drel threat-models your AI system, attacks it and fixes it in your repo, before it ships. Ship AI you can prove.
Built on established AI security frameworks
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.
Turn AI architecture into a defensible security decision.
Point Drel at your repository. It threat-models the system, attacks it in your CI and proposes the fixes in your Jira tickets. The result is a security clearance with clear requirements, owners and sign-off, defensible in front of an AI Committee, regulator or board.
Security review built for AI systems.
Review RAG pipelines, agentic workflows, and LLM-powered applications through a security model built around AI architecture, authority, data flows, tools, and human approval.
Designed around AI-specific architecture and risk, rather than forcing AI systems into generic application-security workflows.
From design to clearance, and as the code changes.
One review that models your AI system, attacks it, fixes it and keeps it cleared.
Your AI system, rebuilt from its repository and design docs.
Every AI system wants to reach production. Someone has to sign off.
See how Drel reviews an AI, RAG or agentic system and turns it into a clearance decision your AI Committee can sign, in under three minutes.
A clearance decision backed by evidence.
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.
- Agent tools match the approved scopeagents/outage_agent.py
- Model access grants only the agreed actionsiam/assistant-role.json
- Prompt-injection filter before retrievalGRID-142 · closes before go-live
Security reviews that end with a decision.
Drel turns AI system reviews into defensible clearance decisions, linking blockers, evidence, ownership, sign-offs, and re-assessment triggers into one audit-ready record.
Clear what can ship
Turn review evidence into a clearance decision: approved, conditional, restricted pilot, on hold, or declined.
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-assessment 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-assessment 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.
Built for the people responsible for AI security.
Whether you're a security architect running a solo review or an AI governance lead producing evidence for a committee, Drel gives you the security clearance and the evidence behind it.
Clear AI systems on your own.
Produce a defensible clearance decision with the controls and evidence behind it. No committee required.
Explore for security architects →AppSec EngineersAI review, inside your SDLC.
Map AI-specific risks and controls into your existing AppSec workflow and control library.
Explore for AppSec →AI Governance & DPOsEvidence your committee can sign.
Give committees the security clearance, control gaps and evidence they need to sign, for ISO 42001 and the EU AI Act.
Explore for governance →Topic guides
The standards your team already uses.
Every threat, control, and remediation item maps directly, so the output lands in your assessment without translation.
Common questions before the first review.
- What is an AI security review?
- A structured, evidence-backed evaluation of an AI system before it reaches production: a threat model specific to the system's architecture, the controls required to address each threat, the evidence gaps still open, and a clearance decision. Not a slide deck, and not a vendor's self-attestation.
- How is this different from a penetration test?
- A penetration test probes a system that already exists, in a running environment, for exploitable vulnerabilities. An AI security review happens at design time, before or alongside build, and produces a control plan and a clearance decision rather than a findings list. The two are complementary, not substitutes: a design-time review misses what only shows up at runtime, and a pentest misses training-data and disposition-level risks that never appear as a runtime exploit.
- What decision does a review produce?
- One of five states: proceed (unconditional), conditional (proceed with named conditions), restricted pilot only (limited deployment with a re-assessment trigger), hold (more work required before any deployment decision), or decline. A binary pass/fail model doesn't capture how AI systems actually clear review in practice.
- Does Drel certify AI systems?
- No. Drel produces a structured, evidence-backed assessment: the record of what was reviewed, what was accepted, and under what conditions. Certification is a separate, accreditation-based process performed by audit bodies, not by review platforms.
- What frameworks does Drel map to?
- OWASP LLM Top 10, OWASP Agentic Top 10, ISO 42001, EU AI Act (Article 9 risk management and Annex III classification), NIST AI RMF, and MITRE ATLAS, mapped to what the specific assessed system actually does rather than applied as a fixed checklist. Sector-specific frameworks (HIPAA, PCI DSS, GDPR, SOX) apply when the system touches that kind of data or activity.
- Does this replace our AI Committee?
- No. Drel produces the evidence pack and decision memo the AI Committee reviews and signs off on: the input to the Committee's decision, not a replacement for it. Named accountability for accepted residual risk stays with the Committee, not with the tool that produced the evidence.
Ready to secure your next AI system?
See how Drel produces a defensible clearance decision for your AI Committee: threats, controls, evidence, and a go/no-go recommendation.
