Risk management statistics show cyber, third-party, and AI risk rising

At a glance

What this is: This statistics roundup shows that cyber risk, third-party exposure, insider risk, and AI-related threats are now core drivers of enterprise risk management priorities.

Why it matters: It matters because identity, access, and governance teams increasingly have to connect risk oversight to NHI, third-party access, and human identity controls rather than treating them as separate programmes.

By the numbers:

• Nearly 75% of enterprises experienced at least one critical risk event in the past year, and cyberattacks and IT failures account for most critical events globally.
• 85% of financial institutions see moderate to high value from their TPRM programs, benefitting from improved cybersecurity, cost savings, and stronger vendor oversight.
• 73% of institutions have two or fewer full-time employees managing vendor risk, even though more than half oversee 300+ vendors.
• 57% of organizations cite operational risk as a top consideration when monitoring subcontractors, up from 40% in the previous survey.

👉 Read Secureframe's 50 risk management statistics for 2026

Context

Risk management has moved beyond a quarterly review exercise. The article’s central message is that organisations are now dealing with a wider mix of cyber, AI, third-party, insider, and compliance risks at the same time, and that older siloed programmes struggle to keep pace.

For identity and access teams, that matters because many of the highest-impact risks now flow through credentials, delegated access, vendor connections, and privileged users. The governance challenge is no longer just whether a control exists, but whether it is linked to the real risk events that can disrupt operations, expose data, or amplify third-party exposure.

Key questions

Q: How should security teams connect identity governance to risk management and compliance?

A: They should treat identity data as the evidence layer for both risk and compliance. That means mapping users, service accounts, third parties, approvals, and exceptions to risk records, then validating that access still matches business need. When identity and governance stay separate, organizations lose both control visibility and audit defensibility.

Q: Why do third-party identities create compliance risk?

A: Third-party identities extend the trust boundary beyond employees and often outlive the business need that created them. If partner access is not reviewed, logged, and revoked with the same discipline as internal access, regulated data can remain exposed even when the legal agreements are in place.

Q: How do organisations know whether a risk-based awareness programme is working?

A: Look for changes in risky behaviour, not just course completion. Useful signals include lower click-through rates, fewer policy bypasses, reduced sensitive-data sharing, and faster escalation of high-risk users or workflows. If those indicators do not improve, the programme is producing activity, not risk reduction.

Q: Should AI risk management be handled separately from security and identity programmes?

A: No. AI risk management should be integrated with security and identity programmes because AI tools often require sensitive data, system access, and delegated authority. If those permissions are not governed like other privileged access, AI becomes another pathway for data exposure, misuse, or uncontrolled automation.

Technical breakdown

Why risk management programmes break under overlapping cyber and third-party risk

Modern risk programmes fail when they treat cyber events, supplier exposure, AI abuse, and compliance obligations as separate workstreams. That creates fragmented registers, duplicated assessments, and weak escalation paths. In practice, the organisation sees many risks, but cannot connect them to owners, controls, or response thresholds quickly enough to act. The article’s statistics point to a recurring pattern: visibility exists in slices, while decision-making needs a joined-up view across business, technology, and governance. Practical implication: risk teams should map each major risk to a named owner, control set, and response trigger.

Practical implication: risk teams should map each major risk to a named owner, control set, and response trigger.

Third-party risk management depends on access visibility, not just questionnaires

Third-party risk is often discussed as a procurement or compliance exercise, but the real issue is operational exposure through connections, integrations, and delegated access. Vendor questionnaires can identify controls on paper, yet they rarely show whether third-party accounts, API tokens, or connected applications are still active and over-scoped. That is where identity governance intersects with risk management. If a supplier compromise can travel through OAuth apps, shared services, or stale access paths, the risk register is incomplete unless it includes actual access relationships. Practical implication: TPRM needs direct visibility into third-party identities and entitlements.

Practical implication: TPRM needs direct visibility into third-party identities and entitlements.

AI is becoming both a risk source and a risk control layer

The article shows a familiar but important tension: AI increases attack speed and scale, yet it is also being adopted to improve detection and assessment. That means risk management teams have to govern both the use of AI and the risks created by AI-enabled adversaries. For identity programmes, the question is whether AI tools are being given access to sensitive systems, data, and decision workflows without the same governance applied to other privileged systems. Practical implication: AI-enabled risk processes need explicit controls on access, data use, and accountability.

Practical implication: AI-enabled risk processes need explicit controls on access, data use, and accountability.

NHI Mgmt Group analysis

Risk management is now an identity governance problem as much as a GRC problem. The strongest risks in this article flow through access, delegation, and third-party connectivity, not just through policy language. That means identity teams cannot treat risk management as an external reporting layer. They have to connect access governance, privileged access, and lifecycle controls to the organisation’s actual risk taxonomy. Practitioners should assume that unmanaged access paths are now core risk signals, not edge cases.

Access-path visibility gap: the real failure is not that organisations lack risk registers, but that they often cannot see the identities and connections driving the risk. The article’s third-party statistics point to a common governance blind spot: suppliers are assessed, but their live access is not fully tracked. That is especially relevant for NHI, where OAuth apps, service accounts, and API credentials can persist beyond business need. Practitioners should tie risk oversight to actual access inventories.

AI-driven risk management will only be credible if the controls around AI systems are treated as governance requirements, not optional safeguards. Organisations are already using analytics and automation to improve risk response, but those same systems can widen exposure if they are granted broad data access or unclear decision authority. This is where AI governance and identity governance intersect. Practitioners should treat AI-enabled workflows as controlled systems with explicit access, logging, and ownership.

Risk maturity will increasingly be judged by operational linkage, not by the existence of a programme. The article shows many organisations have some form of ERM, yet far fewer describe it as mature or strategically useful. That gap usually appears when risk identification, control mapping, and incident response live in separate teams. The practical conclusion is simple: if a risk cannot be tied to a control, owner, and measurable response path, it is not governable enough to rely on.

What this signals

Risk programmes will increasingly be judged on whether they can see beyond enterprise-level categories and into the identities that actually carry exposure. Where third-party access, service accounts, and delegated permissions are not visible, risk oversight becomes descriptive rather than preventive.

Access-path governance debt: this is the accumulation of unmanaged vendor connections, stale credentials, and uncoupled approvals that make risk registers lie about real exposure. The practical signal for practitioners is to connect risk reporting with NHI lifecycle control and identity inventory, then test whether high-risk access can be removed as quickly as it is approved.

The next maturity step is not more reporting volume, but better linkage between risk, control, and ownership. Teams that can show which identities, third parties, and AI-enabled workflows sit behind top risks will be able to prioritise remediation with far more confidence than teams relying on periodic questionnaires alone.

For practitioners

• Map risk events to live access relationships Link your risk register to the identities, integrations, and privileged accounts that can actually trigger the event. Include third-party accounts, service accounts, and connected applications in the same inventory so the register reflects real exposure rather than abstract categories.
• Separate supplier assessment from supplier access review Use vendor questionnaires for baseline assurance, then verify active access paths, OAuth grants, API keys, and delegated roles on a recurring basis. Treat any supplier with persistent access as a higher-risk entity until that access is narrowed or removed.
• Define thresholds for AI and cyber risk escalation Set measurable triggers for when a risk becomes operationally material, such as repeated control failures, unreviewed privileged access, or recurring third-party exceptions. Make sure the threshold forces action from both security and business owners.
• Tie response plans to ownership and evidence Assign each top risk to a named owner, a required evidence source, and a response playbook. This is especially important for insider risk, third-party exposure, and AI-related use cases where the first sign of trouble may appear in access logs rather than in the risk register.

Key takeaways

• Risk management is becoming inseparable from identity governance because many enterprise risks now flow through access, delegation, and third-party connectivity.
• The article’s statistics show persistent gaps in third-party oversight, response maturity, and AI-related risk handling across organisations.
• Practitioners should link risk registers to live access controls, ownership, and lifecycle review so governance reflects actual exposure, not just documented policy.

Key terms

• Integration Risk Management: Integration risk management is the discipline of discovering, classifying, monitoring, and removing risky SaaS connections. It focuses on the permissions and data flows between applications, which often matter more than the vendor’s public-facing posture when attackers exploit delegated access.
• Third-Party Risk Management: Third-party risk management is the process of evaluating and controlling the risks introduced by suppliers, partners, and service providers. It includes assessing their security posture, but also verifying the access, data flows, and dependencies that allow their risks to affect your environment.
• Insider Risk Signal: An insider risk signal is a recurring behaviour pattern that may indicate misuse, negligence, or process breakdown involving sensitive information. It is not proof of malicious intent on its own, but it does show where identity, behaviour, and data handling controls may be misaligned.
• Risk Register: A risk register is a structured record of identified risks, their likelihood, impact, owners, and response plans. In identity-heavy environments, it should include the identities and access paths that create the risk, not just the business system name, so the register can drive real remediation.

What's in the full report

Secureframe's full blog covers the operational detail this post intentionally leaves for the source:

• The complete 50-plus-statistic breakdown across risk categories, useful if you need source-by-source benchmarking for board or audit reporting.
• The article’s step-by-step framework for creating a risk management plan, including how Secureframe groups identification, assessment, treatment, and monitoring.
• The GRC workflow examples for tracking risks, assigning owners, and linking risks to controls in a single system.
• The vendor’s FAQ section on compliance, insider risk, and AI impacts if you want a broader programmatic perspective.

👉 Secureframe's full article includes the complete stat set, risk management guidance, and GRC workflow examples.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It is designed for practitioners who need to connect identity controls to broader security and governance programmes.