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SUMMITGUARD
AI Agent Runtime Governance Review

What can your AI-enabled systems actually access, trigger, approve, log, and unwind?

AI agents and AI-enabled workflows are moving beyond chat into documents, inboxes, SaaS platforms, APIs, and business processes. Summit Guard helps security and technology leaders move from AI policy to runtime control, reviewable evidence, and practical accountability.

Runtime governance model

Access → Action → Approval → Evidence → Rollback

Runtime governance asks whether each step is understood, constrained, reviewable, and recoverable.

Access

Systems, data, documents, tools, workflows, and permission paths the AI-enabled system can reach.

Action

Actions the system can trigger, change, approve, submit, update, or influence.

Approval

Where human review, escalation, or blocking should occur before higher-risk actions.

Evidence

What is logged, retained, and reviewable after AI-assisted decisions or actions.

Rollback

How incorrect, unauthorised, or harmful actions would be detected, contained, and unwound.

Who it is for

For leaders connecting AI to real workflows.

This review is designed for CISOs, Heads of Cyber, CIOs, CTOs, and risk leaders responsible for safe AI adoption.

AI tools are being connected to documents, inboxes, SaaS platforms, workflows, APIs, or business systems.

AI agents, copilots, or AI-enabled automation may influence decisions or trigger actions.

Executives are asking whether AI-enabled workflows are controlled, reviewable, and recoverable.

Security, technology, and risk teams need a practical action plan before broader rollout.

What you receive

Practical outputs, not advisory fog.

01

Runtime governance gap map

Where current controls do not match runtime risk.

02

Tool, data, and system access map

What AI-enabled workflows can reach or influence.

03

Human approval and escalation model

Which actions should be allowed, reviewed, or blocked.

04

Logging and evidence trail assessment

What evidence is retained and where gaps exist.

05

Priority control recommendations

Practical improvements ranked by risk and effort.

06

30/60/90-day action plan

A clear path for improving control without stalling adoption.

How the review works

Step 01

Map access and action pathways

Identify where AI-enabled systems can access data, call tools, trigger workflows, influence decisions, or interact with business systems.

Step 02

Identify governance and control gaps

Review permission boundaries, approval points, escalation paths, logging, reviewable evidence, and rollback assumptions.

Step 03

Prioritise practical next steps

Produce a leadership-ready summary, priority control recommendations, and a 30/60/90-day action plan.

30 / 60 / 90 day plan

A clear path from discovery to repeatable control.

30 days

Map priority workflows, access paths, high-risk actions, and immediate allow / review / block decisions.

60 days

Strengthen approval points, permission boundaries, evidence requirements, and operational review practices.

90 days

Establish governance rhythm, leadership reporting, detection, and rollback assumptions.

Engagement boundary

Request a scoping conversation.

Identify where your AI-enabled workflows may need stronger control before they scale. Delivered as a fixed-scope advisory engagement, quoted after the initial scoping conversation.

This is a practical governance and control review, not a legal opinion, formal audit, certification, or penetration test.