A senior-led advisory engagement for companies where AI oversight now spans legal, risk, security, procurement, data, product, and business owners. We design the federated governance model, committee rhythm, documentation framework, monitoring design, and audit-ready process that makes AI accountable across functions.
This is the right next step when a startup-style policy pack is too light, but a full managed platform or private AI build is premature.
The Enterprise AI Control Pack is for companies with multiple teams, vendors, models, frameworks, and approval paths. The problem is no longer "write a policy." The problem is deciding who owns which AI decisions and how evidence stays current.
Risk, legal, security, procurement, data, and product all have partial ownership, but no operating model ties them together.
Inventories, model reviews, vendor diligence, security findings, and board reporting live in different places or do not exist yet.
Executives need a process they can explain to customers, auditors, regulators, insurers, and the board without pretending AI is fully solved.
We define how AI decisions move through the business: what central governance owns, what business teams own, what security and data teams approve, and what must be escalated.
The output is built so a company can operate it after the engagement: clear owners, defined ceremonies, review evidence, and practical monitoring requirements.
Decision rights, RACI, intake path, risk tiers, approval triggers, and escalation rules.
Charter, agenda, evidence review rhythm, board/risk reporting inputs, and operating cadence.
AI inventory, system profile, vendor review, policy lifecycle, risk register, exception log, and change-control templates.
What to watch, who reviews it, what triggers a re-review, and how model/vendor changes get documented.
How evidence is assembled for customer security reviews, audits, supervisory exams, and board oversight.
A prioritized plan for the first 90 to 180 days, including owners, dependencies, and decisions to defer.
We start with the governance problem and then work down into evidence, controls, and monitoring. The engagement is scoped to the number of teams, systems, frameworks, and committees involved.
Review AI inventory, policies, committees, vendor process, risk registers, and production AI workflows.
Define decision rights, control ownership, committee structure, intake, review, and escalation model.
Build the framework for inventory, risk tiering, vendor review, system profiles, exceptions, and monitoring evidence.
Deliver the operating cadence, templates, roadmap, and leadership readout so the model can keep moving.
We make the distinction explicit so buyers understand what they are buying and what requires a separate workstream.
The homepage range is $75,000 to $250,000+. Final scope depends on the number of business units, AI systems, frameworks, vendors, committees, and evidence workflows involved.
The Growth Pack is a fixed-fee governance baseline for multi-team companies. Enterprise Control is for complex organizations that need decision rights, committee operation, evidence architecture, and monitoring design across functions.
We can help design and support the committee, but the client must own final decisions and risk acceptance. If ongoing ownership is needed, that becomes Managed AI Governance or vCAIO work.
No. Private AI architecture, deployment, and managed AI operations are separate workstreams. Enterprise Control can define the governance requirements those systems must satisfy.
No. We prepare a defensible operating model and evidence process, but we do not certify compliance, provide legal advice, guarantee audit results, or guarantee regulator outcomes.
You can run the model internally, convert into Managed AI Governance, scope vCAIO support, or proceed into implementation/private AI work once governance requirements are clear.
These guides explain how enterprise governance becomes decision rights, committee cadence, implementation rules, and operational evidence.
Scope, membership, approval rights, escalation, evidence, and risk acceptance for enterprise AI decisions.
How owners, tiers, vendor controls, and evidence cadence mature before committee work becomes necessary.
The monitoring, maintenance, model change review, and evidence upkeep expected after launch.
Tell us which AI decisions are already cross-functional, what external pressure is driving the work, and which teams need to participate. We will confirm whether Enterprise AI Control is the right first engagement.
DSE provides advisory AI governance and readiness consulting. We do not provide legal advice, certify compliance, guarantee audit or regulator outcomes, operate a 24/7 SOC/MDR, or accept business risk on a client's behalf. All engagements are governed by a signed SOW / MSA.