30-Day AI Agent Assurance Sprint

Get control of AI agents before they become operational risk.

A focused, founder-led sprint for organisations deploying GenAI, copilots or autonomous agents. In 30 days, we identify what exists, map autonomy and tool access, define human oversight, assess control gaps and produce a board-ready evidence pack using Gamut.

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The problem this sprint solves

AI agents are moving from experiments into business workflows. They can retrieve data, call tools, draft messages, trigger tickets, update records, recommend decisions and operate with changing levels of autonomy. Many teams are deploying them before they have a reliable record of ownership, access, approvals, control gaps and evidence.

You cannot govern what you cannot see

The first gap is visibility: which agents exist, who owns them, what they do and what systems or data they can reach.

Autonomy changes the risk

An agent that can act, delegate, retrieve, write or trigger workflows needs stronger oversight than a chatbot answering questions.

Evidence is often missing

Boards, buyers, auditors and legal teams need proof of controls, not just a policy saying controls should exist.

What we do in 30 days

Week 1: Discover and prioritise

Identify AI agents, copilots, GenAI workflows and AI-enabled SaaS features. Confirm owners, purposes, data context, users and business impact.

Week 2: Map autonomy and access

Document autonomy levels, tool access, data exposure, system touchpoints, external actions, approval requirements and escalation paths.

Week 3: Assess controls and evidence

Review governance controls, human oversight, logging, runtime decisions, security boundaries, supplier dependencies and existing evidence quality.

Week 4: Produce the assurance pack

Configure the Gamut workspace, prepare the control gap report, evidence pack and leadership summary, then agree next steps.

Sprint deliverables

  • AI system and agent inventory
  • Agent ownership and accountability map
  • Autonomy and tool-access map
  • Data exposure and system access review
  • Human-in, human-on and human-over-the-loop model
  • Approval gate and escalation recommendations
  • Control gap report
  • Evidence quality review
  • Risk register and remediation priorities
  • Board-ready AI assurance summary
  • Gamut workspace configured for ongoing assurance
  • Recommended next-stage roadmap

What the board-ready pack answers

  • What AI agents or AI-enabled workflows exist?
  • Who owns them?
  • What are they intended to do?
  • What data, systems and tools can they access?
  • What can they do without approval?
  • Where is human oversight required?
  • What controls are already in place?
  • Where are the evidence gaps?
  • What are the highest-priority risks?
  • What should leadership approve, fund or challenge next?

How Gamut is used during the sprint

The sprint is not a spreadsheet exercise. Gamut becomes the working environment for the assurance activity, so the organisation finishes with structured records, evidence and a configured operating layer.

Agentic CISO

Used to capture agents, autonomy, ownership, access, approvals, data flows, incidents, tests and evidence.

GTSAF control depth

Used to assess governance, security, evidence, oversight, resilience and risk controls with practical depth.

Evidence workspace

Used to organise evidence requests, quality ratings, findings, remediation and reporting outputs.

Who should buy this sprint?

AI SaaS and agent vendors

Prepare for enterprise buyer questions about governance, oversight, controls, evidence and operational assurance.

CISOs and security teams

Get visibility over agent access, tool use, data exposure, shadow AI and control gaps.

Risk, legal and compliance

Turn agent governance from informal discussions into reviewable records and board-ready evidence.

Why this is the right first step

It is focused

The sprint does not try to boil the ocean. It selects the agents, workflows or business areas that matter most and produces usable evidence quickly.

It leads naturally into platform adoption

By the end of the sprint, the Gamut workspace already contains your initial inventory, risks, evidence and assurance outputs, making continued use practical rather than theoretical.

Founder-led delivery

The sprint is led by Arinze Okosieme, creator of Gamut AI and a cybersecurity and AI assurance practitioner with 27+ years' experience, CISSP, CCSP, CCZT and TAISE credentials. The focus is practical assurance, not generic AI governance theatre.

Start the sprint

Give leadership an evidence-based view of AI agent risk in 30 days.

Use the first call to confirm the right scope, target systems, expected outputs and whether the sprint is the right fit.

Book a call