How should organisations measure AI ROI when Shadow AI is present?

Start by treating unknown AI use as part of the cost and risk denominator, not as an edge case. Inventory sanctioned tools, shadow applications, and agentic workloads, then measure discovery coverage, ownership, and business impact together. If usage is hidden, ROI will look artificially strong until the first control failure or audit challenge.

Why This Matters for Security Teams

AI ROI becomes misleading as soon as shadow ai sits outside the measurement boundary. A tool that looks efficient in procurement reporting may be expensive in reality if it is duplicating licensed capability, bypassing controls, or creating invisible data exposure. That is why ROI for AI must be measured as a portfolio problem, not a clean-room finance exercise. Current guidance from NIST Cybersecurity Framework 2.0 is useful here because it ties outcomes to governance, identification, protection, detection, response, and recovery rather than to software spend alone.

The hidden cost is not only license overlap. Shadow AI can trigger untracked data movement, unapproved model use, and unmanaged secrets, which turns a promising use case into a liability. NHIMG research on the DeepSeek breach shows how quickly AI-related exposure can scale once sensitive material is embedded in training or operational paths. When those exposures are absent from the ROI model, leaders overstate value and understate the cost of control failures.

In practice, many security teams encounter negative ROI only after a discovery exercise, an audit, or a breach has already exposed the hidden usage pattern.

How It Works in Practice

Start by building a measurement boundary that includes sanctioned AI, shadow AI, and any agentic or automated workload that can act on data, tools, or secrets. For each item, assign an owner, map the data it touches, and classify whether it is human-prompted, workflow-embedded, or autonomous. That matters because autonomous systems can amplify both productivity and risk without obvious user initiation.

Then measure ROI on three axes: value created, control cost, and residual risk. Value created includes time saved, cycle time reduction, and output quality. Control cost includes discovery, policy enforcement, access review, logging, prompt filtering, and secrets handling. Residual risk includes data leakage, license duplication, model misuse, and governance gaps. This is where NHI controls become relevant: if the AI workload uses secrets, tokens, or API keys, the ROI model should include provisioning, revocation, and monitoring costs as first-class line items. The operational lesson from the DeepSeek breach is that unmanaged AI paths can create an expensive cleanup burden long after the original use case looked profitable.

• Track discovery coverage: how much of the AI footprint is actually known.
• Track ownership: who can approve, suspend, or retire each workload.
• Track control density: how many security controls sit around each use case.
• Track incident-adjusted value: reported gains minus remediation and downtime.

For governance, align the measurement model with NIST Cybersecurity Framework 2.0 and treat hidden AI as a detection problem as much as a finance problem. When the organisation cannot reliably enumerate AI usage, ROI should be reported as provisional rather than final. These controls tend to break down in BYOAI environments because business teams can adopt tools faster than security can discover, classify, and govern them.

Common Variations and Edge Cases

Tighter measurement often increases overhead, so organisations have to balance precision against the cost of chasing every experimental tool. That tradeoff is real, especially where teams use many low-value pilots. Best practice is evolving, but current guidance suggests using tiered measurement: lightweight tracking for low-risk experimentation, and full governance for anything that handles customer data, regulated data, or secrets.

Agentic systems create a second edge case. If an AI agent can chain actions, call tools, or use credentials on behalf of a business unit, its ROI cannot be isolated from the cost of JIT access, workload identity, and policy evaluation. The same is true when shadow AI is embedded in third-party products: the organisation may be consuming AI without seeing a clean vendor line item, so procurement data alone will undercount the real footprint. NHIMG’s coverage of the DeepSeek breach is a reminder that hidden AI usage can become a governance issue before it becomes a budget issue.

There is no universal standard for this yet, but the practical rule is simple: if the organisation cannot tie a workload to an owner, a data class, and a control set, its ROI should be treated as incomplete. That approach also fits the accountability direction in NIST Cybersecurity Framework 2.0 and avoids rewarding invisible risk. In highly decentralised environments, the model often fails because adoption outpaces both discovery tooling and policy enforcement.

Related resources from NHI Mgmt Group

• When should organisations prioritise runtime guardrails over model-focused AI controls?
• How can organisations prove their AI controls are actually working?
• What makes agentic AI an NHI governance issue?
• Why is NHI governance critical in the age of AI attacks?