AI teams move quickly. Security teams often can’t keep up—especially when systems rely on retrieval, agents, and complex trust boundaries that traditional threat modeling doesn’t cover. That’s the gap Drel is built to close. Drel provides AI Security Reviews before production, helping you produce a defensible review in one sitting with evidence-backed threats, a control plan, and a clearance decision your stakeholders can sign.
If you’re responsible for AI governance, application security, or security architecture, you’ve likely seen the same pattern: assessments pile up, answers are inconsistent, and sign-off arrives late—if it arrives at all. Drel is designed for the real work security teams face when AI ships: proving what’s at risk, how you’ll control it, and what must be fixed before go-live.
Pre-production security reviews that hold up under scrutiny
Drel focuses on producing a structured clearance outcome rather than a generic report. Instead of only describing risks, the platform ties each finding to supporting evidence grading and produces a decision you can export and defend. The goal is simple: make your review suitable for an AI committee, regulator, or board—without weeks of manual effort.
With Drel, you review the system intake, the agentic architecture, and the operational boundaries that matter most. You get a clearance path that can be proceed, conditional, restricted pilot, hold, or decline—paired with clear blockers that prevent unsafe deployments.
Built for RAG, agents, and the trust boundaries that break models
Generic AppSec workflows weren’t designed for LLM trust boundaries, retrieval authorization, or agent tool use. Drel is purpose-built for AI, RAG, and agentic systems, working alongside the model providers, cloud platforms, and engineering tools you already use. That matters because the security questions are different: the system doesn’t just generate text—it retrieves data, applies permissions, and may take actions via tools.
For example, Drel’s security review can surface threats like prompt injection via documents and ACL bypass at the retrieval layer, along with the specific validation approach needed to prevent exploitation. The platform also maps findings across common frameworks, so your committee discussions stay aligned and actionable.
Threats, evidence, and controls—organized for sign-off
Drel is not about collecting screenshots and writing narratives after the fact. The platform drives a structured workflow: identified threats, supporting evidence categories (explicit, inferred, assumed, unknown, missing, or verified), and required controls with owners and deadlines. It also highlights evidence gaps that must close before go-live, which reduces the back-and-forth that typically derails approvals.
Just as important, Drel creates a sign-off chain across security and governance roles, including CISO, AI governance, DPO, and business ownership. That audit trail is versioned and timestamped, making it easier to answer “what changed?” when models, prompts, or retrieval systems evolve.
Controls that trigger reassessment when systems change
AI security isn’t static. Drel supports reassessment triggers so changes to your AI system don’t quietly invalidate the review. When your configuration, data sources, retrieval pipeline, or agent tools shift, Drel helps you determine whether the risk posture still holds—and whether new blockers must be addressed.
This “review-to-clearance” approach lets teams ship with conditions rather than shipping blind, keeping velocity while reducing risk.
Conclusion
Drel helps security and product teams perform AI Security Reviews before production with a defensible clearance decision, evidence-backed findings, and a control plan built for RAG and agentic systems. If you want faster reviews that security leaders can sign, Drel is the path from threat modeling to approved go-live decisions—starting with a clear demo at https://drel.ai/.
Reach out to Drel when you’re ready to make AI security review a repeatable clearance process.