Pre-production AI Security Review

AI Security Reviews.
Before AI ships.

Produce a defensible security review of your AI system in one sitting. The threats, the supporting evidence, and a clearance decision you can export and sign. Ready for your AI committee when you have one.

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Internal RAG Assistant

SharePoint · ServiceNow · Azure OpenAI

RAGMCPSSO

Identified threats

Prompt injection via documents

ACL bypass at retrieval layer

100+ threats scanned12 frameworks

Clearance

Conditional

Evidence

Output filteringPending

ACL enforcementPending

Approval boundaryPending

Sign-off

CISO

AI Gov

DPO

System Intake

AI Security Review

Security Clearance

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.

The problem

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

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.

Purpose-built

Built for AI, not generic AppSec.

Built specifically for RAG pipelines, agentic workflows, and LLM-powered features. Purpose-built for AI security review, not generic AppSec workflows or one-off generated reports.

What you get

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.

Clearance decision: proceed, conditional, restricted pilot, hold, or decline

Evidence on every claim: explicit, inferred, assumed, unknown, missing, or verified

Production blockers with required controls, owners, and deadlines

Re-assessment triggers that fire when AI systems change

Multi-stakeholder sign-off chain: CISO, AI Governance, DPO, Business Owner

Audit-ready record: versioned, timestamped, framework-mapped

Internal RAG Assistant

AI Security Assessment Pack · Generated in 4s

Go with conditions

Go-live blockers

Indirect prompt injection via SharePoint documents

Validation: Inject adversarial instructions into a SharePoint document and verify the RAG system does not execute them

Threat register

Indirect Prompt Injection

OWASP LLM01

Critical

Retrieval ACL Bypass

MITRE AML.T0054

High

Identity Propagation Failure

MAESTRO L3

High

Excessive LLM Agency

OWASP LLM08

Medium

Recommended controls

prevInput sanitization pipeline before retrieval

prevACL enforcement at chunk retrieval layer

detePrompt injection detection classifier

Security questionnaire

Is prompt injection mitigated?Pending

Are retrieval ACLs enforced?Pending

Is PII redacted before LLM?Confirmed

Is output logged and auditable?Confirmed

121

Attack paths modeled

Prompt injection, tool-chain escalation, approval bypass, confused deputy. Pre-modelled for every AI system type.

Clearance states

Proceed, conditional, restricted pilot, hold, or decline. Every review ends with a decision, not a report.

Frameworks mapped

OWASP LLM Top 10, NIST AI RMF, EU AI Act, ISO 42001, and more. Linked to every decision.

Differentiation

Security reviews that end with a decision.

Drel turns AI system reviews into defensible clearance decisions, linking blockers, evidence, ownership, sign-offs, and re-review triggers into one audit-ready record.

Clearance

Clear what can ship

Turn review evidence into a clearance decision: proceed, conditional, restricted pilot, hold, or decline.

Blockers

Expose what blocks release

Separate advisory findings from production blockers, missing gates, and unresolved assumptions.

Evidence

Grade the evidence

Track whether each claim is explicit, inferred, assumed, missing, or verified.

Audit Record

Leave the audit trail

Capture rationale, owners, sign-offs, versions, and re-review triggers in one defensible record.

Coverage

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.

Assistants & RAG

Internal RAG assistant
Customer-facing chatbot
LLM gateway

Agents & automation

Agent with tools
Agentic automation
Multi-agent workflow

Product & vendor AI

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.

System types

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.

Internal RAG Assistant

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.

OWASP LLM01Indirect prompt injection via indexed documents

MITRE AML.T0054Retrieval ACL bypass — chunks returned without permission check

MAESTRO L3Identity not propagated from user session to retrieval layer

See a real assessment example

Internal RAG Assistant — Architecture

Agent with Tools

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.

OWASP LLM08Excessive agency — write actions without human approval gate

MITRE AML.T0051Tool call injection via adversarial user input

MAESTRO L5Privilege escalation through chained tool invocations

See a real assessment example

Agent with Tools — Architecture

B2B SaaS AI Feature

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.

OWASP LLM06Tenant data isolation failure — context bleeds across sessions

MITRE AML.T0048PII exfiltration via LLM output in shared inference

NIST AI RMFMissing go-live evidence for enterprise security assessment

See a real assessment example

B2B SaaS AI Feature — Architecture

Who uses Drel

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 structured clearance record you need.

Security Architects

Run a structured AI security review solo. No committee required. Get a defensible clearance decision in one sitting.

For security architects →AppSec Engineers

Integrate AI security review into your existing secure development lifecycle. Map AI-specific risks to your control library.

For AppSec engineers →AI Governance & DPOs

Produce the structured evidence your AI Committee signs. Clearance decisions, control gaps, and evidence packs for ISO 42001 and EU AI Act.

For governance leads →

Topic guides

AI security reviewThe end-to-end clearance workflow

RAG security assessmentIngestion, vector store, retrieval, prompt assembly

Agentic AI securityTool surfaces, delegation chains, blast radius

MCP security reviewModel Context Protocol deployments