Managed AI Security and Red Team Pod

Hire an AI Security and Red Team Pod
Test LLM, RAG, and Agent Systems Before Attackers Do

A managed pod for AI security testing: threat modeling, prompt injection, data leakage, tool misuse, excessive agency, RAG weaknesses, supply-chain review, guardrail testing, and remediation support.

Design my AI security pod See the pod structure

Scope-first onboarding

No blind staffing

Senior technical review

Architecture, QA, delivery

Weekly proof cadence

Demos and decision logs

Built for CTOs who need controlled delivery

Built for CTOs who need controlled delivery

Built for CTOs who need controlled delivery

Built for CTOs who need controlled delivery

Built for CTOs who need controlled delivery

Scope-first pod design

Senior technical review

Weekly demo cadence

Access and IP control

Why AI security fails when teams only test traditional application paths

LLM apps, RAG systems, and agents introduce risks that ordinary web testing misses: prompt injection, tool misuse, data leakage, weak retrieval boundaries, and uncontrolled action authority.

What breaks

The application passes normal security checks but still allows prompt injection, indirect instructions, or tool misuse through model context.

Agents are granted broad permissions, allowing manipulated inputs to trigger actions beyond the user intent.

RAG systems expose sensitive documents through weak retrieval filters, embedding weaknesses, or poor access-control enforcement.

Model outputs are trusted by downstream code without validation, creating insecure output handling risks.

Security findings are theoretical because tests do not connect attacks to specific remediation tickets and release gates.

How the pod fixes it

The pod threat-models the AI system across users, prompts, tools, retrieval, models, data, APIs, logs, and downstream actions.

Testing covers OWASP LLM risk areas such as prompt injection, sensitive information disclosure, supply-chain risk, data poisoning, improper output handling, excessive agency, and vector weaknesses.

Attack scenarios are tied to business impact, reproduction steps, affected components, and remediation guidance.

The pod validates fixes through regression tests, policy checks, access reviews, and guardrail evaluation.

Your team receives a risk report, test suite, remediation backlog, retest evidence, and security handover notes.

Production risks this AI Security pod is designed to control

This section addresses OWASP Top 10 for LLM Applications, AWS control mappings, NIST AI risk guidance, prompt injection, excessive agency, vector weaknesses, and output validation.

01

Prompt injection

The pod tests direct and indirect prompt attacks through user input, retrieved documents, tool output, uploaded files, and conversation history.

02

Excessive agency

Agent permissions, tool scopes, approval gates, and action validation are tested to prevent unsafe autonomy.

03

RAG data exposure

Retrieval filters, metadata controls, vector indexes, source permissions, and citation behavior are reviewed for leakage risks.

04

Remediation proof

Findings include reproduction steps, business impact, affected surface, fix guidance, and retest evidence.

What is included in the AI Security and Red Team Pod

The pod is designed as a managed delivery unit, not a random bench list. Each role has a clear owner, a review responsibility, and a reason to exist in the delivery model.

Owns cadence and visibility

Delivery Head

Keeps AI security delivery aligned with your roadmap, stakeholders, sprint rhythm, blockers, demos, and decision points.

  • Sprint planning
  • Stakeholder updates
  • Friday demos
  • Risk tracking
Owns technical direction

AI Architect

Defines the architecture, release controls, system boundaries, evaluation approach, and long-term maintainability model for AI security.

  • Architecture review
  • Release gates
  • Risk controls
  • Technical roadmap
Owns core build

Senior Implementation Engineer

Builds the core AI security workflows, integrations, pipelines, APIs, infrastructure, or product surfaces required for production delivery.

  • Core implementation
  • API design
  • Integration work
  • Performance review
Owns foundations

Platform or Data Engineer

Handles the platform, data, deployment, observability, or infrastructure layer that the AI security outcome depends on.

  • Pipelines
  • Infrastructure
  • Observability
  • Operational handoff
Owns validation

AI QA Engineer

Builds test cases, evals, regression checks, edge-case coverage, and release evidence so quality is visible before the system reaches users.

  • Regression suites
  • Eval cases
  • QA gates
  • Quality dashboards

Pod size: 4-6 people depending on AI security scope, platform risk, compliance needs, and the amount of internal support already available.

How the AI Security and Red Team Pod moves from scope to proof

The process is built to reduce ambiguity before engineering effort compounds. You see the pod design, approve the key people, and get a working proof point before the engagement turns into a long commitment.

How the AI Security and Red Team Pod moves from scope to proof
Discovery and risk mapping

Discovery and risk mapping

We map your product goal, current stack, internal team, stakeholders, data or system access, constraints, timeline, and the decision this AI security pod must make easier.

Pod design

Pod design

We recommend the pod composition, seniority mix, delivery model, communication cadence, review checkpoints, and first sprint scope. The pod is shaped around your risk profile, not a fixed package.

Shortlist and alignment

Shortlist and alignment

You review the Delivery Head or technical lead and any critical specialist roles. We explain why each person fits the work, what they will own, and where your internal team stays in control.

Onboarding into your tools

Onboarding into your tools

The pod joins your repositories, documentation, issue tracker, communication channels, cloud or data tools, QA flow, and security process. Access is scoped and documented before sensitive work starts.

Sprint execution and weekly proof

Sprint execution and weekly proof

The pod works in visible sprint cycles with PR review, QA checks, technical notes, and working demos. You see progress through usable increments, not status-only reporting.

Scale, extend, or hand over

Scale, extend, or hand over

You can scale the pod, add specialist coverage, adjust scope, or take a documented handover. Knowledge transfer, runbooks, validation evidence, and decision records remain with your team.

AI Security and Red Team Pod: engagement models

Use these models to compare a focused delivery sprint, an embedded managed pod, and a larger enterprise pod. Final scope is confirmed after discovery so you do not buy roles you do not need.

90-Day Sprint

Red Team Sprint

$25,500

/mo

4-person pod, 3 months

  • →Threat model + report
  • →Adversarial testing + payloads
  • →Guardrails shipped
  • →Compliance mapping
Talk to Sales

Embedded · Most Popular

Embedded Security Pod

$24,000

/mo

4-person pod, ongoing

  • →Continuous red team
  • →Guardrail tuning
  • →Incident response support
  • →Compliance evidence kept current
Talk to Sales

Enterprise

Enterprise AI Security Pod

$38,500

/mo

Regulated / public-facing

  • →Continuous red team + remediation
  • →Customer-facing evidence
  • →Regulator engagement
  • →Dedicated architect
Talk to Sales

When to choose the AI Security and Red Team Pod

Choose this pod when the work needs a managed delivery unit with page-specific ownership, not isolated capacity.

01

Pre-launch LLM app review

Test customer-facing or internal AI features before production rollout.

02

Agent security testing

Review tool use, approval flows, credentials, action validation, and excessive-agency risk.

03

RAG security assessment

Test retrieval boundaries, document permissions, prompt injection through sources, and sensitive-data exposure.

04

AI vendor due diligence

Evaluate third-party AI systems, APIs, data handling, logging, and security controls before adoption.

What the AI Security and Red Team Pod should prove

These are the proof points a CTO or product leader should expect before treating the pod as production-ready.

Threat model

The pod maps users, data, prompts, tools, models, retrieval, logs, APIs, and action paths.

Attack evidence

Findings include reproducible payloads, traces, screenshots or logs where appropriate, and clear impact descriptions.

Control validation

Fixes are retested against the original attack and related variants before closure.

Security handover

Your team gets test cases, risk ratings, remediation guidance, and release recommendations.

AI Security and Red Team Pod vs other hiring options

The pod model is a middle path between unmanaged staff augmentation and black-box project outsourcing. You keep product direction and repository control while Devlyn adds role coverage, delivery cadence, technical governance, QA, and replacement support.

POD vs freelancers

AI Security and Red Team Pod gives you continuity, role coverage, weekly accountability, and documented handover. A freelancer can be useful for a narrow task, but AI security work usually needs architecture, implementation, validation, QA, and operating discipline moving together.

POD vs in-house hiring

In-house hiring gives long-term control, but it can take months before the full team is productive. A Devlyn pod starts faster, works inside your tools, and can transfer knowledge back to your internal team as the roadmap stabilizes.

POD vs individual staff augmentation

Staff augmentation works when your managers can absorb more people. A pod is better when you need a managed delivery unit with a Delivery Head, technical review, QA rhythm, and a shared outcome instead of scattered individual availability.

POD vs generic outsourcing

Generic outsourcing can hide work until a milestone review. A Devlyn pod runs in visible sprints, joins your communication flow, shows working software, and keeps code, documentation, and decision history inside your operating model.

Ready to design your AI security pod?

Share your roadmap, current team structure, stack, constraints, and delivery goals. We will help you decide whether a AI Security and Red Team Pod is the right model, what roles it should include, and what proof should exist before you commit to a longer engagement.

Design my AI security pod Book a pod strategy call

hello@devlyn.ai

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7-day risk-free trial

Senior technical review

Same-day response

Frequently Asked Questions

Direct answers for buyers comparing this pod against individual hiring, staff augmentation, and traditional project outsourcing.

A AI Security and Red Team Pod is a managed delivery unit assembled around AI security outcomes. It combines the relevant specialists, senior oversight, QA, delivery rituals, documentation, and governance needed to move the work from plan to production while your team keeps product direction and control.

Hiring individuals gives you capacity, but your leaders still own role design, onboarding, architecture, review, QA, delivery cadence, and replacement risk. This pod gives you a structured team with clearer ownership across implementation, validation, reporting, and handover.

Yes. The pod tests direct and indirect prompt injection, tool misuse, excessive agency, insecure output handling, sensitive information exposure, and RAG-specific weaknesses.

The pod can do both. We produce clear findings and can work with your engineers to implement controls, retest fixes, and add regression tests so the issue does not return.

It should prove that key attack paths have been tested, high-risk findings have remediation plans, access boundaries work, and sensitive actions cannot be triggered without the required controls.

Most pod engagements can begin alignment within days once scope, access, and commercial terms are clear. The first practical milestone is a scoped onboarding plan covering repositories, tools, stakeholders, risk areas, and the first proof point.

Yes. For critical roles such as technical lead, delivery lead, architect, or specialist engineer, you can review fit before onboarding. The goal is controlled team formation, not anonymous staffing.

The pod has delivery ownership through a lead or delivery manager, while your team keeps product direction, priorities, repositories, and final decisions. Communication cadence is agreed during onboarding.

Yes. The pod can join your existing backlog, standups, planning, code review, QA process, release workflow, documentation, and communication channels.

Quality is handled through role ownership, senior review, pull requests, QA checks, working demos, documentation, evals where relevant, and clear release criteria. The exact controls depend on the pod type.

Your organization retains ownership of product direction, repositories, code, credentials, and final decisions. Access is scoped, credentials remain controlled, NDAs can be signed, and handover documentation stays with your team.

Yes. The pod can be expanded, narrowed, or reshaped as the roadmap changes. We recommend changing the pod based on delivery evidence, not guesswork.

We define replacement and escalation paths before the engagement scales. If a person is not the right fit, the issue is addressed without forcing you to redesign the entire team.

Most pod work can be structured as a focused sprint, embedded ongoing pod, managed delivery pod, or specialist extension. The right model depends on the outcome, risk, internal ownership, and timeline.