RINP // CYBERSECURITY SERVICES
Offensive AI Enablement · AI-FOR-PT · Internal Leverage
Bring AI into your offensive security teams with expert oversight and measurable quality.
We design AI workflows with expert oversight for penetration testing and Red Team teams. First we define the use case and the authorizations. Then we run a measurable pilot with evaluation set, logging, approval, and rollback controls.
- The priority use case is selected from your existing offensive security workflow.
- Human approval, least privilege, and observability form the core controls of the workflow.
- A controlled pilot, evaluation set, and handover package establish a measurable working standard.
4 stages
- 01
Discovery & hypothesis
Accelerated, expert-supervised
- 02
Evidence → finding
Approval model + action matrix
- 03
Reporting & retest
Evaluation set, acceptance gates
- 04
Controlled pilot
Observability + handover pack
Human-supervised · controlled speed
What this service is, and is not
- It supports the repeatable steps of your internal penetration testing and Red Team teams with AI under expert oversight.
- It designs the workflow with an approval model, least privilege, an evaluation set, and logging and rollback controls.
- It transfers the controlled pilot results to the organization through an executive summary, a technical implementation guide, and a handover package.
- Not the Generative AI Red Team; the runtime chain on the prompt, tool, RAG and MCP surface of the customer's GenAI product opens a different decision moment.
- Not AI Model Supply-Chain Assurance; the model's source provenance, component list and attestation trust are a separate decision and a separate service.
- Not setting up a fully autonomous attack agent, SOC automation, AppSec SDLC transformation, from-scratch model training, 24/7 operation or contact with unauthorized assets; no workflow runs without human expert oversight and written authority.
Scope and boundaries
- Priority offensive use case (1–2) and workflow design
- Threat model, risk register and safe execution boundaries
- Approval model and least-privilege action matrix
- Evaluation set (eval set), test scenarios and acceptance logic
- Logging, audit trail and observability design
- Rollback and emergency-stop logic
- Controlled PoC or pilot execution
- Implementation guide, measurement set, handover pack and next-sprint plan
- Customer GenAI product security testing within the Generative AI Red Team scope
- Model trust-chain assessment within AI Model Supply-Chain Assurance
- Fully autonomous attack-agent setup, automated exploit generation or expert-out-of-the-loop operation
- SOC automation, incident response or defensive-operations automation
- AppSec SDLC transformation or code-review process design
- From-scratch model training, general AI product development or 24/7 operation
- Unauthorized third-party contact, ambiguous RoE or uncontrolled privileged production action
- Written authorization, rules of engagement (RoE), stop criteria and rollback coordination are approved.
- An internal pentest and Red Team team exists; the priority use case and target owner are defined.
- Previous reports, findings, evidence samples, the tools used, repo info and target-surface logic are shared.
- Customer data is not exfiltrated; sensitive samples are processed on a minimum-data principle; the default retention is 30 days or adjusted by contract.
- The starting price is an approximate budget; the final proposal narrows once use-case maturity, artefact variety and action level are clear.
- Priority use case and target owner: discovery, hypothesis, evidence-to-finding, retest or multi-step orchestration
- The current pentest/Red Team workflow, target-surface logic and previous report/finding/evidence samples
- Tools used and repo info; technical and business owners; an allowed window and RoE notes
- Where possible, report templates, finding patterns, a measurement set and the current operations guide
- Optional: bilingual-delivery expectation, regulatory framework, previous AI risk assessment reports, on-site work request
The pilot is run with written authorization, limited data, human approval, least privilege, logging, stop conditions, and a rollback plan.
How we work: six steps from use case to controlled rollout
Kick-off, RoE & use-case selection
Written authorization, rules of engagement (RoE), stop criteria, rollback coordination, the priority use case and the target owner are clarified; the starting view is drawn.
Workflow map & data/artefact/target-surface inventory
The current pentest and Red Team workflow (discovery, hypothesis, evidence handling, reporting, retest) is read; artefact families (previous reports, screenshots, notes, PoC evidence, ticket/wiki/operations guide/target list/report template) and target-surface logic are mapped.
Threat model, approval model & control design
For the selected use case, we design the threat model, the human approval points, the least-privilege action matrix, and the logging and rollback controls.
Evaluation-set design & PoC development
We prepare the evaluation set and acceptance criteria, then run the workflow under expert oversight in a controlled PoC environment.
Hardening, acceptance & observability validation
We evaluate the pilot results against the acceptance criteria and verify the authorization, approval, logging, observability, and rollback controls.
Pilot acceptance & handover pack
We deliver the executive summary, technical implementation guide, measurement set, and handover package, and we prepare a roadmap for the next use cases.
What we deliver
Executive summary
- Use case summary: explains the current bottleneck, the proposed workflow, and the pilot result.
- Benefit and risk picture: shows the expected time savings, the human approval, and the limits of use.
- Roadmap: lays out the next use cases and the required controls.
Technical design and implementation guide
- Workflow design: defines the steps, the owners, and the acceptance criteria.
- Approval and authorization matrix: shows which step is approved by whom and with which authorization.
- Evaluation set: provides the quality and security criteria together with test scenarios.
- Implementation guide: explains the logging, monitoring, rollback, and emergency stop steps.
Optional outputs
- Offensive Workflow Design Workshop: a workshop, as a separate line, for clarifying scope and the use case.
- Retest Acceleration Sprint: an extra line to set up the retest workflow in a separate sprint.
- Extra use-case discovery/development, bilingual delivery, on-site work or accelerated delivery is priced as a separate line when needed.
Management sees the expected benefit and the limits of use. The offensive security team follows the workflow, the human approval, the measurement set, and the operating steps.
Decision profile
- Duration
- 2–6 weeks (by package and scope)
- Rhythm
- Sprint delivery by package choice
- Delivery
- Management + technical
- Scope
- Use-case unit measure
- Best for
- Organizations with an internal pentest/Red Team practice
Which is the right start, and when?
These three AI lines do not produce the same evidence object. Starting without knowing the difference wastes time and budget chasing the wrong proof.
| Criterion | ÖnerilenAI for Pentest & Red Team | Generative AI Red Team | AI Model Supply-Chain Assurance |
|---|---|---|---|
| Decision question | As offensive-delivery speed rises, are expert oversight and delivery quality preserved? | At runtime, which chain truly breaks, and which control cuts it? | What do we truly trust about the model and the release pipeline? |
| Primary evidence object | Workflow design, approval model, eval set, controlled pilot, observability, handover pack | Prompt → tool → data → authority/MCP chain, abuse path, agent behavior, chain-breaking control | Source provenance, component list (BOM), attestation, registry/release trust, evidence pack |
| Ideal trigger | The internal team's delivery time is growing; report production consumes analyst time; AI use started but approval/logging is not written down | Go-live of a GenAI product; adding a new tool, connector or MCP; agent authority rising to write/execute | A new model version; onboarding a vendor/open-source model; an evidence-pack need for customer review/audit |
| Wrong match | Attempting to test the customer's GenAI product instead of internal offensive-workflow design | Meeting a runtime-chain need with internal offensive-workflow design | Meeting a model-trust-chain need with internal offensive-workflow design |
One example of decision clarity
Anon case
As delivery time grows: how are expert oversight and delivery quality preserved?
An organization's internal offensive security team gave us a scope to accelerate the repetitive work in reporting and retest preparation using AI under expert oversight. Together we defined the use case, the data limits, and the approval points.
By reviewing the existing workflow, we built the least-privilege action matrix, the evaluation set, and the observability measures. We ran the controlled pilot against the acceptance criteria without using real client data.
We delivered the executive summary, the technical implementation guide, and the handover package. Preserving the approval and quality controls, the team adopted the selected workflow into its own offensive security process.
What this case produced
- The selected workflow was accelerated with human approval.
- The authorization, logging, and rollback controls were defined in writing.
- The pilot was handed over to the team with the implementation guide and the measurement set.
Let's clarify your internal offensive-workflow scope together
In this form we clarify the use case, the current workflow, the data limits, the authorizations, the approval points, and the acceptance criteria.
Frequently asked questions
This service improves the workflow of your internal offensive security team. GenAI Red Team, on the other hand, tests the client's generative AI product.
Model Supply Chain Assurance examines the origin of the model and its distribution pipeline. This service focuses on the penetration testing and Red Team team's own delivery process.
We do not build a fully autonomous attack agent and we do not run external client testing. Critical decisions and the final security finding and report approval remain with the expert team.
SOC automation, incident response, SDLC transformation, and continuous operations are outside the scope of this service. The work focuses only on internal penetration testing and Red Team workflows.
The priority use case, the current workflow, sample reports, the tools in use, the data limits, the technical owners, and the authorized working conditions should be shared.
Client data is not taken out. Human approval, least privilege, logging, monitoring, rollback, and emergency stop controls are applied.
Related services and add-ons
If your need extends beyond AI for Pentest & Red Team, open the right bridge here.
Application Security Penetration Test
If the focus is evidence-based deep validation of the internal pentest team's web, API and mobile delivery pipeline, the Application Security Penetration Test is the right start.
RTModular Red Team Simulation
If the focus is the internal Red Team team's attack-path production line, detection-gap validation and crown-jewel impact, the Modular Red Team Simulation is the right start.
PT-5Continuous Penetration Testing
If the focus is the retest and closure-visibility discipline of an internal team under a continuous release rhythm, Continuous Penetration Testing is the right start.
// AI-FOR-PT · DISCOVERY
Let's clarify the use case, the human approval, and the pilot scope together.
In the discovery call, we define the current workflow, the data limits, the action authorizations, the acceptance criteria, and the expected handover package together.