Building the Control Plane for ERP, Finance, and SaaS
AI didn’t come with a rollout plan; it crept in unnoticed. Someone turned on a copilot in a finance or CRM application, an IT team tested an agent on a non‑production system that still contained real audit data, or a regional team started using an AI assistant to push invoices and approvals faster, all without going through central access governance.
None of those decisions felt big on their own. Still, together they created an invisible workforce of systems acting on behalf of people: invoking APIs, touching production data, and making changes without the governance policies those same enterprises rely on for human access. At the same time, these AI agents are moving highly sensitive information—financial, customer, and operational—through prompts, embeddings, and integrations that often sit outside traditional data‑protection controls.
The result is a very concrete set of risks: misposted transactions, unauthorized changes to master data, uncontrolled movement of regulated data, and audit findings you only discover after the fact. AI governance can no longer be just about the model; it must unify data governance and identity governance in the systems where these risks materialize.
AI risk is now an identity and data problem
AI is already inside your ERP, finance, and SaaS stack—drafting journal entries, touching customer records, wiring itself into collaboration tools, and orchestrating workflows across APIs. According to the latest 2026 CISO‑level AI risk research, a majority of security leaders say AI has access to core business systems; however, only a small fraction believes that access is effectively governed. At the same time, regulations such as the EU AI Act are pushing organizations to prove how AI‑related data is controlled, traced, and protected throughout its lifecycle.
The control gap is stark. Many organizations lack full visibility into their AI identities and doubt they could detect or contain misuse quickly if an agent went rogue. Teams still treat agents as just another piece of software, skipping clear identity rules and leaving fast, automated access controlled only by application settings and fragmented logs. In parallel, AI initiatives often treat data governance as a one‑time box‑check at model training, instead of a continuous discipline that limits which sensitive data AI can see and act on in production.
Machine and AI identities already outnumber human users in many environments, and regulators are increasingly asking not only whether AI is “trustworthy,” but also who approved its access to financial and regulated data in the first place. This is why AI risk has become both an identity and a data problem.
Perimeter controls do not follow headless agents into cloud platforms, and device policies do not apply to serverless workflows or SaaS copilots. Identity is the enforcement layer that stays in place as AI moves across ERP, finance, SaaS, and data platforms—where entitlements, approvals, and audit trails converge—while data governance defines which datasets and fields are sensitive, regulated, or restricted in the first place.
Without an identity‑centric control plane that understands data sensitivity, AI governance is just policy on paper: frameworks and principles with no reliable way to enforce least privilege, segregation of duties, or data‑minimization for non‑human actors. With a control plane in place, leaders gain real‑time visibility into AI identities, the sensitive data they can touch, and precise, policy‑based access control—along with board‑ready evidence that AI is operating inside defined risk boundaries, not outside them.
The new AI identity and data landscape
The identity model has shifted from “users and roles” to a mixed landscape where humans, machines, and agents all act on your systems and data. Human employees now work with AI copilots embedded across productivity suites, CRM, ticketing, and vertical SaaS, which in turn invoke APIs and back‑end services on their behalf. Underneath, machine identities and service accounts connect LLMs, orchestration engines, data platforms, and transaction systems, often with elevated or hard‑coded permissions that are rarely reviewed.
On top of that, autonomous agents and built‑in SaaS AI features execute workflows end‑to‑end: reading from ERP, enriching with CRM or data‑warehouse context, and then writing back to finance, case management, or shared content stores. Recent AI‑risk studies show that many organizations have already discovered unsanctioned AI tools in their environment, often with embedded credentials or high‑privilege access that security never signed off on. A large majority report that AI systems can request, automate, or otherwise influence identity and permission changes, yet non‑human identities are still ranked among the “least risky” in many programs.
Every one of those identities and agents also has a data footprint: which tables, documents, reports, prompts, or embeddings it can see, and which of those datasets include regulated elements like supplier bank accounts, customer credit limits, or personal data. These non‑human identities show up exactly where your blast radius is highest.
If you still treat AI identities like regular service accounts, you’re underestimating both their power and their risk. In this world, any AI governance approach that does not start with both identity and data loses the ability to answer the most basic forensic question: who (or what) did what, to which data, where, and under which policy.
The Model Context Protocol: where AI, data, and identity intersect
As enterprises move from isolated copilots to agentic AI, the Model Context Protocol (MCP) is emerging as a common pattern for connecting models to tools and enterprise systems. MCP defines how AI models discover and call external tools and data through MCP “servers” and a common message format, so agents can read from ERP, query data warehouses, or update SaaS applications without bespoke integrations each time.
From a governance perspective, MCP is where your AI, data, and identity decisions meet. An MCP server is effectively a doorway that decides which data sources and tools an AI agent can see, under which scopes and parameters; misconfiguration can expose broad access across systems that were never designed to be used together. Because MCP consolidates access to multiple services into a single protocol layer, it changes the security model: instead of one application talking to one system, you have agents that can reach many systems through a single, powerful integration point.
That makes MCP both a powerful enabler and a new blast‑radius multiplier. If an MCP server exposes financial tables, HR datasets, and collaboration tools behind a single agent, then identity, Segregation of Duties (SoD), and data‑classification rules need to be enforced at the MCP boundary, not only inside each downstream application. Early security guidance suggests treating MCP servers as privileged integration services, keeping an inventory of approved servers, and enforcing least-privilege tokens and scopes.
What’s missing in many organizations is a way to tie those MCP decisions back into a central view of which AI identities exist, which MCP servers they can reach, and which sensitive data and business actions that combination unlocks.
SafePaaS viewpoint: SafePaaS treats MCP not as a black‑box connector, but as part of the AI identity and data control plane. AI agents that connect through MCP are onboarded as first‑class identities, with policies that define which ERP roles, SaaS permissions, and data‑classification tiers they can access, and which MCP servers are in scope. By correlating MCP access, entitlements, and data sensitivity, SafePaaS helps enterprises adopt MCP‑based AI workflows—especially in finance and operations—without losing the ability to enforce least privilege, SoD, and audit‑ready evidence at the point where AI, data, and identity converge.
Why AI governance must start with effective identity and data governance
Nearly every material AI failure mode ultimately traces back to identity, access, and data. Data leakage occurs when an agent is granted broad, unmonitored access to sensitive datasets. Fraud and mis‑postings emerge when copilots can execute in financial flows that were designed for tightly controlled human roles. Compliance violations follow whenever AI can bypass SoD or operate outside approved jurisdictions and data‑processing policies.
Traditional Identity and Access Management (IAM) excels at authentication, MFA, and coarse‑grained authorization—but it is not designed to model fine‑grained SoD patterns, correlate risk across thousands of entitlements, or continuously certify AI identities across ERP, finance, and SaaS. Privileged Access Management (PAM) protects privileged sessions and secrets, yet many AI agents operate via API keys, service principals, or SaaS integrations that sit outside classic privileged user flows.
In parallel, traditional data governance and catalog tools classify and trace data, but they rarely control which AI identities can act on that data in business‑critical workflows. AI data governance must define which datasets can be used for which purposes, under which regulatory and retention constraints; AI identity governance must ensure only authorized humans and agents can invoke those datasets and actions.
Identity governance fills this gap by operating one layer above IAM and PAM. It defines which human and non‑human identities can exist, which policies and SoD rules apply, how approvals and certifications work, and how evidence is generated over time. It turns AI governance principles—transparency, accountability, least privilege, and data minimization—into enforceable controls that can be tested, monitored, and attested at the same velocity as the agents they cover.
SafePaaS viewpoint: SafePaaS provides this enforcement layer as a single, policy‑based platform to govern identity and access across business‑critical applications, turning identity governance from a collection of projects into an ongoing control enterprises can rely on. That same approach extends to AI identities and their data footprints, so policies, SoD, data‑access restrictions, and certifications apply uniformly to humans, machines, and agents.
The AI governance control plane: where it sits
Most enterprises already run a layered security and governance approach. At the bottom is the systems layer where real business transactions and data access occur: ERP, financial platforms, CRM, collaboration tools, and line‑of‑business SaaS. Above that sits IAM and PAM, providing authentication, SSO, MFA, session management, and secrets management for both human users and non‑human identities.
Alongside those layers, data governance programs define sensitive data domains, classify and catalog datasets, track lineage, and enforce retention and masking policies—capabilities that are essential for complying with regulations like the EU AI Act and sector‑specific guidance. But those programs rarely see, in one place, which AI identities can actually reach those datasets or what actions they can take once they do.
The identity governance control plane sits above IAM and PAM and in tight alignment with data governance, tying this ecosystem together. It normalizes entitlements and events across applications into an identity‑centric view, so each human, machine, and agent has a single, authoritative profile that captures what it can do, which sensitive data it can touch, and why. This plane maintains the AI identity inventory, models SoD and least‑privilege rules, maps those rules to data‑classification tiers, orchestrates approvals and certifications, and continuously monitors for policy violations and drift.
For security and IT leadership, the outcome is straightforward: a single place to see, govern, and prove control over all AI‑related identities and their access to critical data, regardless of where they run.
SafePaaS viewpoint: SafePaaS implements this control plane as a policy‑based platform that centralizes entitlements, access models, certifications, and identity lifecycle events across the enterprise. That same foundation now extends naturally to AI agents and machine identities, so you can govern AI access to both business transactions and sensitive data with the same rigor you apply to high‑risk human roles.
What security leaders need from the control plane
At the leadership level, the control plane must answer a small set of uncomfortable but essential questions with evidence, not opinion. How many AI identities do we actually have—agents, copilots, integrations, service accounts—and where do they live? Which of them can touch financial systems, PII, or other regulated datasets, and under what conditions and approvals? Where are we exposed to over‑privilege, toxic combinations, shadow AI, or risky data flows that have crept into production via toggles and user‑driven integrations?
To do that, the control plane needs a central AI identity inventory with risk classification, spanning human‑adjacent, machine, and agent identities. It must support policy‑ and SoD‑driven access decisions for AI identities, not only for human accounts, and enforce those policies consistently. And it must integrate with data governance programs so that identity policies reflect data classification and locality rules, ensuring AI agents cannot move sensitive or regulated data into unapproved contexts.
Metrics turn AI identity and data governance from a project into a performance conversation. Directionally, for a 10,000‑employee enterprise, centralizing AI identity governance can reduce manual review effort by 30–40% while increasing coverage of high‑risk AI access. Useful examples include the percentage of AI identities under governance, the proportion of high‑risk AI access requests approved versus denied, the number of AI‑related SoD and data‑policy violations detected or prevented, and time‑to‑contain for anomalous AI activity.
SafePaaS viewpoint: SafePaaS delivers this view through policy‑driven federated identity governance that covers both human and non‑human identities, with end‑to‑end access controls, risk scoring, and continuous monitoring. By tying identity, entitlement, and data‑sensitivity information together, SafePaaS allows leadership teams to move from anecdotal statements about AI exposure to hard metrics and auditable evidence.
What CIOs need: safe speed, not slowdown
On the IT side, the mandate is to scale AI safely while avoiding a trailing wave of technical debt and audit findings. AI pilots have already proliferated; the challenge now is turning them into a governed portfolio of production services that can be supported, monitored, and upgraded without breaking risk posture. That requires a consistent way to onboard new AI use cases, align them to existing architectures, and keep identity, access, and data governance under control as platforms change.
Identity governance enables safe speed by providing standard policies and role patterns for AI use cases that can be reused across applications and vendors. Rather than negotiating access rules from scratch for every copilot or agent, IT teams can apply pre‑approved patterns—such as “read‑only analytics,” “draft‑only in finance,” or “restricted contact scope in CRM”—implemented as roles and SoD models in the control plane, combined with data‑access constraints that respect existing classifications and residency rules.
The same control plane provides clean, consistent evidence for internal audit, external auditors, and regulators across all AI use cases. Over time, AI shifts from disconnected experiments to a managed portfolio of services governed under a common identity‑and‑data framework, reducing integration friction while tightening control.
SafePaaS viewpoint: By integrating directly with ERP, SaaS, IAM, and data‑governance programs, SafePaaS lets IT teams apply uniform policies and policy‑based access models to AI identities, while providing out‑of‑the‑box workflows and evidence aligned to ITGC/ITAC expectations. That shortens the path from AI idea to production rollout without sacrificing auditability.
Operating model: who owns what
A durable AI identity and data governance model depends on clear ownership. Security leadership owns the overall AI risk and control strategy, defining the policies, control objectives, and risk appetite for AI identities and their access to sensitive data. IT leadership owns the architecture and enablement of the control plane—how identity governance integrates with ERP, SaaS, IAM, and data platforms.
An AI governance committee or council brings security, IT, legal, risk, data governance, and business stakeholders together to approve high‑risk AI use cases and adjudicate exceptions. Application and data owners define the business rules, sensitive‑data classifications, and SoD requirements for their domains, including how AI can interact with ledgers, customer data, and regulated workloads. Core processes include AI use‑case intake and risk assessment tied into identity and data governance workflows, lifecycle management for AI identities (joiner‑mover‑leaver for agents and machine accounts), and regular certifications focused on AI access into high‑risk systems and datasets.
SafePaaS viewpoint: SafePaaS embeds these processes into configurable workflows—linking AI use‑case intake, risk assessment, approvals, data‑access checks, and certifications into a single platform—so operating‑model decisions are enforced in day‑to‑day access flows, not just on paper.
Roadmap: how to implement an AI identity and data control plane
Phase 1 – Discover and assess
Build an AI identity, data‑flow, and integration inventory, correlating what AI tools and agents exist with where they plug into ERP, finance, and SaaS, and which sensitive data they can touch. Identify high‑risk systems and AI use cases that interact with financial, regulated, or otherwise sensitive data.
Phase 2 – Define policies and models
Design risk‑based identity policies and SoD patterns that explicitly include AI identities, not just human roles. Align these policies to emerging AI governance frameworks such as NIST, ISO/IEC 42001, and EU AI Act guidance, and to existing data‑classification policies, so you can map identity and data controls to recognized standards.
Phase 3 – Enforce and monitor
Integrate the identity governance control plane with your key applications, IAM, PAM, and data‑governance platforms, so policies can be enforced and events collected across the full stack. Turn on continuous monitoring, alerts, and reporting specifically tuned to AI access and activity, including privilege drift, anomalous behavior, and unexpected data‑movement patterns.
Phase 4 – Optimize and communicate
Use analytics to refine roles and policies, reduce over‑privilege, and close residual gaps as AI adoption grows. Report progress and key metrics into board‑level AI risk reporting, connecting improvements in AI identity and data governance to reduced incident likelihood and stronger regulatory posture.
SafePaaS viewpoint: SafePaaS accelerates this roadmap with prebuilt controls, risk libraries, SoD templates, and automated certification workflows that can be extended to AI identities and data‑access scenarios, shortening time‑to‑value for the control plane.
Questions to ask now
To pressure‑test your current posture, ask yourself and your vendors:
- Do we have a single, up‑to‑date inventory of all AI identities, human‑adjacent, machine, and agent, and the sensitive data and systems they can access?
- Can we produce a clear view of which MCP servers exist, who owns them, and which financial and regulated datasets they expose?
- Can we enforce and prove policy‑based control over AI identities and their data access, or are we still relying on app‑by‑app settings, scripts, and manual reviews?
- What would we tell our board or regulators tomorrow if asked, “How are you governing AI access to your most critical systems and data?”
If the honest answer is “not yet,” the path forward is clear: elevate identity and data governance into a unified AI control plane, and turn AI risk from an abstract concern into an identity‑and‑data problem you can actually solve.
SafePaaS gives you that control plane—a single, policy‑based platform to discover AI identities, govern their access to sensitive data and business processes, and prove to your stakeholders that AI is operating inside the risk boundaries you define, not outside them.
Turn your AI stack from a black box into a governed control plane.
See how SafePaaS gives you a single place to discover AI identities, lock down MCP entry points, and control data access—schedule a live walkthrough with our team.
