Agentic AI Comes for Legacy Risk Platforms. SAP Data Readiness Will Decide What Happens Next.

ServiceNow and accenture are positioning legacy risk platforms as targets for agentic AI modernization, aiming to remove cost and complexity, the two barriers that stall most risk modernization efforts. For SAP teams, the deciding factor will be whether SAP and non-SAP risk data is governed, integrated, and traceable enough to support autonomous workflow actions.

On June 29, ServiceNow and Accenture launched a joint offering built around managed security services running on the ServiceNow AI Platform, and an Accenture AI-powered solution that automates migration off legacy systems. The companies frame the goal plainly: to strip out the cost and complexity of moving off legacy cybersecurity platforms.

The urgency is not abstract. According to ServiceNow, U.S. data breach costs hit an all-time high of $10.22 million per incident in 2025, up 9% year over year. AI, the companies note, has compressed the window between a vulnerability being discovered and exploited from months to mere hours.

“Cyber resilience is a clear business imperative as organizations face a growing volume of threats and increasing operational complexity,” said Rex Thexton, global chief technology officer at Accenture Cybersecurity. “Companies need more than isolated security tools. They need the ability to connect risk insights, automate decision-making, and respond at enterprise scale.”

That matters to SAP practitioners because GRC is being evaluated for agent-mediated detection, triage, remediation, and evidence capture. The gap is equally clear. Many organizations are studying autonomous risk workflows while still running fragmented evidence, manual tracking, and incomplete integration.

What ServiceNow and Accenture Shipped

The offering bundles four named capabilities:

• Unified integrated and third-party risk management, where AI agents monitor vendors and surface a single enterprise risk view
• Operational technology risk management that brings OT and IT risk onto one platform
• Proactive risk and compliance, where agents track regulatory change and automate responses
• AI-powered migration off legacy platforms

“The future of cybersecurity will be driven by autonomous operations powered by AI,” said Lou Fiorello, group vice president and general manager of Security and Risk products at ServiceNow.

The legacy footprint this targets is real. SAPinsider research finds that a third of organizations still run risk monitoring on manual spreadsheets, the compliance equivalent of asking a filing cabinet to improvise.

Consider an SAP-specific case: a segregation-of-duties conflict during a finance close. Traditionally, an analyst checks role data, emails an approver, and records the outcome. In an agentic workflow, the system detects the conflict, evaluates control context, proposes remediation, routes the exception, and documents the evidence chain. Every step depends on the underlying data being trustworthy.

The Data Foundation Is the Constraint

The critical question for SAP teams is not whether an agent can perform a task. It is whether the task is grounded in governed data. SAPinsider research shows more than 40% of organizations have only partially integrated data across SAP and non-SAP systems. Only 15% report unified and governed data availability, and only 11% describe their environment as seamless and intelligent.

Those numbers define the ceiling. An agent that routes an exception, recommends a role change, or prepares an attestation is participating in a control-relevant process. That action requires a record tied to known provenance, ownership, and context. SAPinsider findings show why this is hard: 47% cite data lineage and provenance as a pain point, 40% cite data silos and ownership issues, and 20% still list SAP GRC-to-operational-systems integration as a requirement. These are prerequisites for safe agentic action, not cleanup items for after deployment.

Automation Is Advancing, But Unevenly

Some teams are closer than others. Automation maturity is highest in access control and role provisioning, at 59% and 54% respectively, making access governance a plausible early surface for agentic AI. The broader picture is less mature. SAPinsider finds 80% of organizations self-assess at GRC Maturity Level 3: integrated into business processes, but short of predictive analytics, end-to-end automation, and enterprise-wide integration. Agentic risk workflows generally require capabilities beyond a Level 3 model.

Where foundations exist, the payoff is concrete. SAPinsider associates GRC automation with 70% greater efficiency, roughly 51% faster approvals and exceptions, and about 49% reduced overhead. For instance, a global industrial manufacturer cut audit cycle time 30% after replacing spreadsheet attestations with workflow-based controls. A pharmaceutical firm reduced the control-exception cycle time by about 40% after unifying testing and remediation. Auditability is not a side effect of agentic GRC. It is the requirement.

What This Means for SAPinsiders

Treat data lineage as the gating spec, not a backlog item. SAP and non-SAP risk data flows, lineage, and ownership decide whether an agentic GRC pilot is even scopeable. Before evaluating a single-agent demo, enterprise architects should map where the risk data lives, who owns it, and whether the provenance survives an audit. If lineage is murky, the pilot will stall regardless of how capable the platform is. Start the data-readiness assessment now, in parallel with vendor conversations.

Budget for a multi-year integration program. The ServiceNow and Accenture pitch is about migrating off legacy risk platforms, and the honest framing is that the GRC outcome depends on integration work, not branding. CIOs should fund this as a phased modernization with measurable interim wins, and tie funding to the efficiency benchmarks so the board can see the curve. Pick access governance as the first proving ground, where automation maturity already sits near 60%.

Move the client conversation from agent demos to evidence design. The differentiation is no longer who can show an autonomous workflow; it is who can stand up SoD context modeling, audit-evidence chains, and reversibility that withstand review. SAP systems integrators must reframe early engagements around a data-readiness and control-ownership assessment. That is where SAP clients will either green-light or kill an agentic GRC program, and it is where the system integrator’s expertise is hardest to commoditize.