CISOs in 2026 face a deeper authorization control gap

At a glance

What this is: This is a CISO-focused analysis of ten 2026 security challenges, with the strongest finding being that authorization visibility and consistency remain major control gaps across modern enterprise environments.

Why it matters: It matters because the same access-governance weaknesses affect human IAM, NHI governance, and AI-driven systems, so IAM teams need a shared control model instead of fragmented app-level logic.

By the numbers:

• 70% of CISOs feel at risk of a material cyber attack in the next 12 months.
• 70% of cloud breaches stem from misconfigured access controls.
• An average of 76 security tools creates siloed data and overwhelms analysts with noise.

👉 Read Cerbos’ analysis of the 2026 CISO authorization and compliance challenges

Context

Authorization has become the hidden pressure point in modern identity security. When access rules are embedded in individual applications, security teams lose a reliable way to explain who can access what, which undermines both Zero Trust and auditability. The article frames that problem through the daily reality of CISOs, but the underlying issue is broader: fragmented access control creates governance drift across human users, service accounts, and AI-driven systems.

The challenge is not just more threats or more compliance demands, but the lack of a central decision layer for access. In cloud, SaaS, and distributed application environments, the old perimeter no longer exists, so identity and authorization have to carry the whole security model. That makes policy consistency, reviewability, and evidence generation core governance requirements rather than implementation details.

Key questions

Q: How should security teams centralize authorization across cloud applications?

A: Security teams should separate policy decisions from application code and evaluate access through a central service or policy layer. That gives the organisation one source of truth for entitlements, easier audit evidence, and consistent enforcement across APIs, microservices, and data access paths. It also reduces the risk that different teams implement contradictory rules in different languages or frameworks.

Q: Why do hard-coded access rules create governance risk?

A: Hard-coded rules create governance risk because every application becomes its own access-control island. Policies drift over time, reviews become manual, and auditors cannot easily verify whether the same rule is enforced consistently everywhere. The result is weak assurance, especially in environments where identity, data, and workload boundaries change quickly.

Q: What breaks when Zero Trust stops at authentication?

A: Zero Trust breaks when teams stop at authentication because proving identity does not automatically define what that identity may do. If authorization is still implicit inside applications, a compromised account can still move through systems with excessive privilege. The control gap is not login strength, but decision quality after login.

Q: How do NHI and AI access controls differ from human access control?

A: NHI and AI access controls need stronger runtime enforcement because they can operate faster, more consistently, and sometimes without a human watching each action. Human access programmes rely more easily on sessions, prompts, and user behaviour, while machine identities and AI systems need policy boundaries that apply on every request and every delegated action.

Technical breakdown

Why hard-coded authorization logic breaks governance

Hard-coded authorization means each application contains its own rules for who can do what. That creates drift because policies are duplicated, implemented differently, and changed at different speeds. A central policy decision model avoids that by separating policy from application code, so access decisions can be audited, versioned, and updated once. In identity terms, this is the difference between scattered enforcement and a single source of truth for entitlements. It also matters for compliance because access evidence becomes queryable instead of buried in code reviews and release notes.

Practical implication: move authorization decisions into a centrally managed policy layer rather than leaving them embedded in app code.

Zero Trust depends on contextual authorization, not just MFA

Zero Trust is not achieved by authentication alone. Once identity is verified, each request still needs contextual authorization based on role, attribute, resource, and risk. In distributed environments, static allow lists and network trust are too weak because a compromised identity can move laterally if authorization is broad or implicit. The article points toward policy-as-code and a Policy Decision Point model because those patterns let teams enforce least privilege consistently across APIs, services, and data paths. That is the technical difference between perimeter security and real identity-centric control.

Practical implication: pair strong authentication with request-level authorization that can be evaluated consistently across every application.

AI and automated systems turn authorization into a runtime control

The article treats AI systems and autonomous agents as identities that can take actions on behalf of the enterprise, which means access control cannot stay manual or human-paced. These systems may make decisions faster than review cycles can keep up, so authorization has to be enforced at runtime and tied to policy, not tribal knowledge. The same control problem appears with NHIs, but AI systems increase the urgency because their behaviour can expand or shift during execution. That makes centralized authorization a governance foundation for both machine identities and AI-driven workflows.

Practical implication: treat AI-operated access as runtime identity governance, with explicit policy boundaries and full decision logging.

Breaches seen in the wild

• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.
• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Authorization is now the control plane that determines whether modern identity programmes are governable at all. When access logic lives inside each application, the organisation loses a reliable way to answer the oldest IAM question: who can do what, under which conditions, and with what evidence. That problem spans human access, service accounts, and AI-driven workflows, which is why fragmented authorization is no longer a technical nuisance but a governance failure. Practitioners should treat centralized authorization as the baseline for any credible identity architecture.

Hard-coded authorization is a hidden audit and assurance debt. Every app-specific rule multiplies policy drift, review overhead, and evidence collection work. The result is not just inconsistency, but a programme that cannot easily prove enforcement. That is why the article’s strongest signal is not about a tool choice, but about the governance cost of leaving access decisions scattered across codebases. Practitioners should reframe authorization as a shared control with a lifecycle, not a development convenience.

Zero Trust fails operationally when authorization is still implicit. Authentication can tell you who or what is asking, but it cannot by itself define the allowed action in context. If access is granted by default inside the application layer, the organisation still has a trust boundary that attackers can exploit after initial compromise. This is the point where Zero Trust either becomes enforceable or remains rhetorical. Practitioners should align Zero Trust programmes to request-level authorization, not just stronger login flows.

Runtime access governance is the named concept this article points to: policy must be evaluated where decisions happen. In cloud and AI-heavy environments, the decision point is no longer the login page, it is the request path. That changes how security, platform, and application teams divide responsibility because policy has to be shared, versioned, and observable across systems. Practitioners should design around that runtime boundary rather than assuming static entitlement reviews can keep pace.

AI-era access governance accelerates the convergence of human IAM and NHI controls. The article’s AI discussion is not separate from IAM, it is an extension of the same authorization problem into faster and less predictable execution contexts. Once AI systems can act on behalf of users or services, policy consistency and audit trails have to cover both identities and the actions they trigger. Practitioners should stop treating AI access as a side case and fold it into the main governance model.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
• For a broader control lens, see Ultimate Guide to NHIs , Lifecycle Processes for Managing NHIs for the lifecycle patterns behind access, review, and offboarding.

What this signals

Runtime authorization is becoming the practical bridge between Zero Trust and AI governance. The more identity decisions move into cloud services, AI workflows, and automated operations, the less value there is in treating access as a static entitlement list. Teams that keep authorization close to the request path will have a much better chance of proving control across human and non-human actors.

Cerbos’ framing aligns with a larger market shift: security leaders are moving away from scattered app logic and toward shared policy services that can support auditability, consistency, and faster change. That model is especially relevant where access decisions must survive both cloud sprawl and AI-driven automation. The programme implication is straightforward. Identity teams should plan for one policy layer that serves human users, service accounts, and machine-driven actors rather than three disconnected control stories.

For practitioners

• Externalize authorization from application code Move access decisions into a central policy service so permissions can be reviewed, versioned, and tested consistently across applications instead of being reimplemented in each codebase.
• Map Zero Trust to request-level decisions Tie every sensitive action to contextual authorization rather than assuming authentication or network location is enough to establish trust.
• Create one evidence trail for access decisions Log policy changes, authorization outcomes, and administrative actions in a form auditors and security teams can trace without searching individual applications.
• Bring AI and NHI access under the same policy model Define how service accounts, bots, and AI systems are authorised to act, and make those rules enforceable at runtime rather than in separate exception processes.

Key takeaways

• The article’s core message is that authorization has become the hidden control point for modern identity governance.
• The evidence points to widespread access sprawl, with cloud misconfiguration, tool overload, and AI-era privilege gaps all reinforcing the same problem.
• Practitioners should centralize policy, harden runtime decisions, and fold machine and AI access into the same governance model as human identity.

Key terms

• Authorization Decision Point: The component that decides whether an identity may perform a requested action on a resource. In modern identity architecture, this decision is separated from application code so policy can be managed centrally, tested consistently, and audited without hunting through multiple services.
• Policy as Code: A way of expressing access and governance rules in version-controlled code rather than in ad hoc configuration or application logic. It makes policies testable, reviewable, and repeatable, which is especially useful when the same rules must apply across many services and identity types.
• Runtime Authorization: Authorization that is evaluated when a request happens, not just when a user or system is initially authenticated. For cloud, NHI, and AI-driven environments, runtime evaluation is the difference between fixed trust assumptions and access decisions that can adapt to context.

Deepen your knowledge

Authorization centralization, policy-as-code, and lifecycle governance are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building a unified access model for humans, workloads, and AI systems, it is worth exploring.

This post draws on content published by Cerbos: 10 critical challenges CISOs shared with us in 2026. Read the original.