Do we need both a Jira-integrated security tool and Drel?

For teams with an AI Committee and production AI systems, yes. The Jira-integrated tool handles developer-level feedback inside the SDLC. Drel handles the committee-level clearance decision and the governance artifact that outlasts any individual sprint.

Can our SDLC security tool produce the AI Committee disposition?

The tools in this category do not produce a disposition. They produce suggestions and comments. A disposition requires a decision (approved, on hold, declined…), explicit control requirements, evidence mapping, residual risk acceptance, and a sign-off record — none of which emerge from a ticket workflow.

What does Drel take as input if we already have security tooling in the SDLC?

Drel rebuilds the AI system from its repository and design docs: architecture, model, tools, data flows, deployment context. It works inside the tools you already use: each required control becomes a Jira ticket, with a proposed fix when its check fails. When the ticket is marked done and the control has an automatic check, Drel runs it on the merged pull request and comments the result on the ticket; the control's status stays with your reviewers. Output from existing SDLC security reviews can be uploaded as source artifacts to support specific evidence gaps in the Drel case.

Is this relevant for teams using EU AI Act Article 9 risk management?

Yes. Article 9 requires a risk management system with documented risk assessment, residual risk evaluation, and measures. Ticket-level SDLC suggestions do not constitute a risk management system record. Drel's evidence pack is designed to be that record.

Drel vs. AI security tools in the SDLC

A new category of tools embeds AI security review inside Jira tickets, PR checks, and dev backlogs, reviewing epics and user stories as they move through the SDLC. These tools produce suggestions for developers. Drel works in the same repository, CI and Jira, but on the AI system as a whole: it threat-models the system, checks the controls it can verify in your code, proposes the fixes in Jira tickets, and produces the security clearance the AI Committee signs before production.

A useful category of AI-native security tool has emerged: products that connect to Jira, Confluence, Google Drive, Linear, and GitHub and review engineering tasks, epics, tickets, and design documents as they move through the development lifecycle. They answer developer-shaped questions: “Does this story have a security risk?” “What should we add to the acceptance criteria?” “What does the architecture note miss?”

These are useful tools for developers and AppSec engineers. They are not what an AI Committee needs when it asks: “Should this AI system reach production, under what controls, and who accepts the residual risk?”

For a broader, sourced comparison of where SecurityReview.ai, Prime, and Seezo rank against Drel and the rest of the AI security review category — including honest gaps, not just Drel's strengths — see Best AI Security Review Platforms in 2026.

The fix lives in Jira; the decision lives with the AI Committee. Drel puts each control in a Jira ticket with its acceptance criteria, proposes the fix when the control's check fails, checks the merged pull request, and gives the committee a security clearance with the evidence behind it and who signed it.

What SDLC-embedded AI security tools do well

Surface security considerations inside the developer's existing workflow — Jira, Linear, GitHub — without requiring a context switch.
Review user stories, epics, and design documents for obvious risk patterns as they are written.
Assign inline security suggestions that developers can act on before a PR is opened.
Aggregate historical context — past tickets, previous designs, related decisions — to avoid repeating the same mistakes.
Integrate with CI/CD and PR workflows so security feedback arrives at commit time.

This is valuable, especially for teams shipping AI features inside existing products. It moves security left without requiring a separate process.

The governance gap they leave

SDLC-embedded tools review the building process. They do not produce the artifact the AI Committee needs at the end of that process.

The output is developer-facing, not committee-facing. Inline suggestions and Jira comments are the right format for a developer. A CISO, AI Governance officer, or DPO needs a Decision Memo: a decision, the rationale behind it, required controls grouped by lifecycle gate, residual risk acceptance, evidence gaps, and a sign-off log. That artifact does not emerge from a ticket stream.
The unit of analysis is a ticket, not a system. AI security risk lives at the system level — the interaction between model, tools, retrieval surface, data handling, and autonomy. Individual stories and tickets do not capture the system-level risk profile. An agent that looks safe story-by-story can be dangerous at the system level.
Framework mapping is absent or shallow. AI Committee obligations under ISO/IEC 42001, the EU AI Act Article 9, NIST AI RMF, and OWASP Agentic Top 10 require mapped evidence at the control level. A ticket comment cannot serve as evidence of control coverage in an audit.
There is no clearance decision. The SDLC review process produces a series of improvements — not a decision. Someone still has to decide whether the AI system should reach production, on what conditions, and with what residual risk on record. That decision needs a process, an artifact, and a signature. SDLC tools do not produce it.

Side by side

Capability	Drel	SDLC-embedded AI security tool
Primary output	Security clearance: threat model, attack paths, control plan, evidence pack, sign-off log.	Inline suggestions, ticket comments, security annotations on stories.
Primary reader	AI Committee: Security Architecture, AI Governance, DPO, Business Owner and CISO delegate.	Developers, AppSec engineers inside the dev workflow.
Unit of analysis	The AI system — model, tools, retrieval surface, data handling, autonomy.	User story, epic, ticket, design document.
Clearance decision	Explicit disposition: Approved / Conditional / Restricted pilot / On hold / Declined.	Not produced — the process improves tickets, it does not issue a decision.
Framework mapping	ISO/IEC 42001, EU AI Act Art. 9, NIST AI RMF, OWASP Agentic Top 10, AIUC-1.	OWASP LLM Top 10 if at all; rarely mapped to AI governance frameworks.
Evidence pack	Structured evidence record per control — citable in audit, exportable as PDF.	No structured evidence record; tickets are not audit artifacts.
Sign-off workflow	Per-role sign-off: CISO delegate, AI Gov, DPO, Sec Arch, business owner.	Not produced.
Re-assessment triggers	Typed events: model change, tool added, autonomy increase, vendor change.	Triggered by next PR or next epic — not system-level lifecycle events.
Where it lives	In the repository, CI and Jira the AppSec team already uses, with the clearance signed by the AI Committee in Drel.	Inside Jira, GitHub, or Linear — part of the dev backlog.

How the two should coexist

These categories complement each other. The right model is sequential, not competitive:

Drel owns the pre-production security clearance. Before the AI system reaches production, Drel threat-models it, checks the controls it can verify in your code, and produces the security clearance with its required controls and evidence requirements. This is the AI Committee's record of the decision.
Drel's Jira tickets carry the control IDs. Drel creates a ticket per required control with its acceptance criteria, proposes the fix when the control's check fails, and checks the merged pull request when it is done. The SDLC tool can track the same tickets at story level.
Changes reopen the clearance. Drel re-checks the automatic checks you turn on when the branch you link changes. When a developer adds a new tool or changes the model, Drel flags the system for re-review, Drel re-analyzes only the attack paths and controls the change reaches, and the AI Committee's decision is revisited, not just a ticket updated.

Frequently asked questions

Do we need both a Jira-integrated security tool and Drel?: For teams with an AI Committee and production AI systems, yes. The Jira-integrated tool handles developer-level feedback inside the SDLC. Drel handles the committee-level clearance decision and the governance artifact that outlasts any individual sprint.
Can our SDLC security tool produce the AI Committee disposition?: The tools in this category do not produce a disposition. They produce suggestions and comments. A disposition requires a decision (approved, on hold, declined…), explicit control requirements, evidence mapping, residual risk acceptance, and a sign-off record — none of which emerge from a ticket workflow.
What does Drel take as input if we already have security tooling in the SDLC?: Drel rebuilds the AI system from its repository and design docs: architecture, model, tools, data flows, deployment context. It works inside the tools you already use: each required control becomes a Jira ticket, with a proposed fix when its check fails. When the ticket is marked done and the control has an automatic check, Drel runs it on the merged pull request and comments the result on the ticket; the control's status stays with your reviewers. Output from existing SDLC security reviews can be uploaded as source artifacts to support specific evidence gaps in the Drel case.
Is this relevant for teams using EU AI Act Article 9 risk management?: Yes. Article 9 requires a risk management system with documented risk assessment, residual risk evaluation, and measures. Ticket-level SDLC suggestions do not constitute a risk management system record. Drel's evidence pack is designed to be that record.

Produce the governance artifact, not just better tickets

The clearance decision the AI Committee signs is a different artifact from what SDLC tools produce. Drel builds it from your repository and design docs, inside the CI and Jira your team already uses.

Get a demo

A note on scope: Drel reviews assessed systems against documented architecture, configuration and intent. It does not ingest live telemetry from production environments. Dispositions reflect the assessed system at the time of review and the re-assessment triggers that govern when the disposition must be revisited.