Top AI security risks in 2026 are really identity risks

At a glance

What this is: This webinar argues that AI security risk in 2026 is largely an identity and access problem, with OAuth abuse, shadow AI, and NHI sprawl driving exposure.

Why it matters: It matters because IAM, IGA, PAM, and NHI teams now have to govern AI through existing access pathways rather than treating models as a separate control plane.

By the numbers:

• AI-related attacks have increased nearly 490 percent year over year.
• 80 percent of AI-related incidents involve regulated or, ated or sensitive data.

👉 Register for Grip Security's webinar on the top AI security risks in 2026

Context

AI security is increasingly an access-governance problem, not just a model-security problem. In SaaS-heavy environments, AI features inherit permissions from the applications and identities they connect to, which means the real control point is the identity and access layer that already exists inside enterprise tooling.

This webinar frames shadow AI, OAuth abuse, and non-human identity sprawl as connected risks rather than isolated events. That is a useful correction for security teams, because it places AI governance inside IAM, IGA, and PAM operating models instead of creating a separate and competing control stack.

The primary topic is typical of modern enterprise environments: AI adoption is spreading faster than ownership, visibility, and enforcement can keep pace. That is exactly the pattern most identity programmes are now confronting across human users, service accounts, and agent-like workloads.

Key questions

Q: How should security teams govern AI features embedded in SaaS applications?

A: Treat embedded AI as a machine identity problem with data access implications. Inventory the feature, map the connected permissions, define what data it may use, and monitor retention and sharing paths. If the AI feature can read corporate content, it needs explicit approval, logging, and periodic review like any other privileged integration.

Q: Why do OAuth integrations increase AI security risk?

A: OAuth turns access into a delegated relationship that can persist across applications and data sets. When AI features use those grants, the permissions often exceed what any human would approve interactively, which increases exposure if the integration is mis-scoped, forgotten, or reused across environments.

Q: What breaks when organisations only monitor AI models and not access paths?

A: They miss the control surface where risk actually propagates. The model may be harmless, but the connected identity can still reach sensitive systems, copy data, or move across SaaS applications. Monitoring the model without governing its access leaves the real attack path untouched.

Q: Who should own governance for AI token usage and SaaS sprawl?

A: Ownership should be shared across IAM, security, and finance, because the problem crosses entitlement control, data exposure, and budget management. IAM can define and revoke access, security can evaluate risk and shadow AI, and finance can monitor consumption and renewal exposure. No single team can close the gap on its own.

Technical breakdown

OAuth token abuse and long-lived access paths

OAuth gives applications delegated access that can persist long after the original business need changes. In AI-enabled SaaS environments, that delegated access can become a durable control plane for data movement across multiple systems. The risk is not the token alone, but the fact that the token often inherits permissions broader than the user would ever approve manually. When AI features sit inside trusted SaaS tools, the permission grant can be invisible to both users and security operations.

Practical implication: audit OAuth grants as standing identity paths, not one-time integrations.

Non-human identity sprawl in AI-enabled SaaS

AI agents, automations, service accounts, and API-linked workloads each create a non-human identity with credentials and access paths that must be governed. The article’s core point is that AI expansion does not create a new security category so much as it multiplies the number of identities already in circulation. Without inventory, ownership, and lifecycle control, these identities become silent escalation points that blend into normal SaaS activity.

Practical implication: inventory every AI-adjacent identity and bind it to a lifecycle owner.

Identity-centric governance for shadow AI

Shadow AI is often embedded inside approved SaaS platforms, which makes discovery harder than for standalone tools. That changes the technical problem from software inventory to control enforcement across identities, permissions, and integrations. If security teams only scan for models or endpoints, they miss the actual propagation path of risk. Governance has to follow the access chain, because the AI feature is only the surface expression of the deeper identity relationship.

Practical implication: enforce control at the identity layer where access is inherited and propagated.

NHI Mgmt Group analysis

AI security risk is now an identity governance problem first and a model problem second. The article is right to frame AI exposure through identity, access, and integration layers because that is where enterprise control actually exists. IAM, IGA, and PAM programmes already know how to reason about ownership, privilege, and lifecycle, but many AI initiatives are still being evaluated as if they were standalone tools. The practical conclusion is that AI governance should be absorbed into identity operating models, not layered beside them.

OAuth and delegated access are becoming the hidden control surface for AI risk. Once an AI feature inherits broad SaaS permissions, the exposure path is longer-lived and harder to see than a user session. That means the governance unit is no longer the app itself but the delegated relationship between the app, the identity, and the data plane. Practitioners should treat OAuth grants as standing entitlements with blast-radius consequences, not as a technical setup detail.

NHI sprawl is the category-level consequence of AI adoption inside SaaS. AI agents, automations, and service accounts are all non-human identities, and they all accumulate access faster than most lifecycle processes can review. The result is a governance gap between identity creation and identity accountability. Security teams need to recognise that AI expansion is multiplying NHI volume, not creating an exception to NHI governance.

Visibility without enforcement is not a meaningful control posture. The article highlights discovery, but discovery alone does not change the access already granted to embedded AI functions. This is where many programmes stall, because they can inventory shadow AI without being able to constrain the permissions it inherits. The field implication is clear: control must reach the integration and permission layer, or visibility becomes reporting rather than governance.

Identity blast radius is the right concept for understanding AI risk in SaaS. AI does not scale risk linearly with adoption; it compounds through inherited access and cross-application connectivity. That makes the governing question less about whether an AI feature exists and more about how far its access can propagate when identity is reused across systems. Practitioners should evaluate AI exposure by blast radius, not by feature count.

From our research:

• 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
• 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, which shows how quickly delegated access can outpace governance.
• That visibility gap connects directly to 52 NHI Breaches Analysis, which shows how identity sprawl and unmanaged credentials turn into breach paths.

What this signals

Identity-first AI governance is becoming the default operating model. The practical shift for security teams is away from model review and toward control of delegated access, ownership, and entitlement scope. With 1 in 4 organisations already investing in dedicated NHI security capabilities, according to The State of Non-Human Identity Security, the market is signalling that governance is moving from theory to programme design.

Shadow AI will stay invisible unless SaaS and identity telemetry are connected. Teams that monitor applications in isolation will continue to miss the cross-system access path that AI features inherit. The right programme response is to connect discovery, recertification, and entitlement enforcement so that visibility turns into revocation when needed.

AI governance now needs the same operational discipline as other NHI controls. The useful question is no longer whether AI is a separate category, but which existing identity processes must be extended to include it. That means lifecycle ownership, access reviews, and blast-radius reduction all become part of the AI control baseline.

For practitioners

• Map AI-adjacent identities Create an inventory of every service account, automation, and agent tied to AI-enabled SaaS workflows. Assign ownership, lifecycle status, and the business purpose for each identity so that access can be reviewed against real operational need.
• Review OAuth grants as standing access Classify OAuth tokens and app permissions as persistent identity paths that can outlive the original approval. Revoke broad or unused grants, and require re-approval when the integration scope changes.
• Bind enforcement to the integration layer Set policy controls on the identity and integration layer where SaaS systems exchange data, rather than relying on visibility into the AI feature itself. Use least privilege, segmentation, and periodic recertification to limit blast radius.
• Separate shadow AI discovery from risk reduction Treat discovery as the start of governance, not the finish. Every uncovered AI feature should be mapped to data sensitivity, access scope, and owner accountability before it is left in production.

Key takeaways

• Top AI security risks in 2026 are mostly identity risks hidden inside SaaS access paths.
• The scale signal is clear: AI incidents are rising fast, and sensitive data is already involved in most cases.
• Security teams should govern delegated access, lifecycle ownership, and entitlement scope before AI adoption expands further.

Key terms

• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• OAuth Grant: An OAuth grant is the delegated permission an application receives to act on a user's behalf without storing the user's password. In NHI governance, it should be treated as a standing identity relationship with scope, ownership, and revocation requirements, not as a one-time setup detail.
• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
• Identity Blast Radius: The amount of damage a compromised identity can cause across systems, data, and infrastructure. In NHI environments, it is shaped by permissions, network reach, and administrative capability rather than by the credential alone. Reducing blast radius is a containment strategy that limits lateral movement and data exposure.

What's in the full article

Grip Security's full webinar covers the operational detail this post intentionally leaves for the source:

• A fuller breakdown of the top AI security risks in 2026 across SaaS, OAuth, and non-human identities.
• Operational examples showing how shadow AI appears inside trusted applications and why it is difficult to govern.
• Practical guidance on reducing access exposure across AI-connected identities and integrations.
• The webinar framing and supporting material behind the research summary used in this analysis.

👉 Grip Security's full webinar covers the SaaS access patterns, OAuth exposure, and AI governance details behind these risks.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.