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Enterprises should manage cloud and AI security as one business-risk program: know what systems and data they depend on, assign accountable owners, control identities and access, make activity visible, and rehearse recovery. Cloud providers secure parts of their services, but customer duties vary by service and configuration. AI adds risks that must be assessed from design through operation and evaluation—not just at launch.
Why cloud and AI security belong in enterprise risk management
Cloud adoption changes where identities, data, workloads, and logs reside. AI introduces systems and dependencies whose risks can change as they are developed, deployed, used, and evaluated. Treating these as isolated technical projects can leave business leaders without a clear view of exposure, ownership, or the consequences of a control failure.
NIST’s IR 8286 Rev. 1, published in December 2025, describes integrating cybersecurity risk information with enterprise risk management (ERM), linking it to mission and business objectives, and communicating risk across system and organizational levels. NIST’s CSF 2.0 ERM Quick-Start Guide (SP 1303, October 2024) likewise describes enterprise-wide risk processes and monitoring across organizational units. These are ways to structure decisions and communication, not proof that an organization is secure or compliant.
For each material risk, decision-makers should be able to see the affected business service, likely impact, accountable owner, treatment decision, and whether the exposure is improving or worsening. That makes technical findings useful to executives without pretending a single score captures security.
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Start with an inventory and named owners
An organization cannot manage what it has not identified. Maintain an inventory that joins technical assets to business purpose and responsibility. Depending on the environment, that means recording cloud accounts, tenants, subscriptions, workloads, data stores, identities, third-party services, and AI systems.
- Assign a business owner who can explain the service’s importance and acceptable disruption.
- Record an operational owner responsible for configuration, monitoring, maintenance, and escalation.
- Classify data and dependencies so teams can identify sensitive information, provider reliance, and connections to other systems.
- Set a risk priority and treatment in the enterprise risk register or an equivalent process, connecting technical exposure to business impact.
For each AI system—whether built internally or procured—record its purpose, users, inputs, model or service dependencies, deployment setting, and lifecycle stage. NIST’s AI Risk Management Framework (AI RMF) covers trustworthiness considerations across AI design, development, use, and evaluation. The framework is voluntary; it does not replace applicable law, contracts, or sector-specific obligations.
Map cloud responsibilities instead of assuming the provider owns them
Security duties are shared, but the division depends on the service and its configuration. SaaS, PaaS, and IaaS do not assign every operational task in the same way. For each service, document who configures, protects, monitors, and recovers each relevant asset or control.
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In particular, establish responsibility for customer-controlled configuration, identities and permissions, data handling, audit logging, incident response, and recovery. Do not infer that a provider’s security measures automatically cover these customer responsibilities. CISA’s ransomware guidance specifically advises organizations to review their cloud shared-responsibility model.
Make the record practical: name the team or provider responsible, describe the expected action, and identify how the enterprise verifies it is being done. Revisit the map when services, integrations, or operating arrangements change.
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Control access through identity and policy
Cloud controls should account for people, applications, services, and workloads—not just the network from which a request originates. NIST SP 800-207A (September 2023) describes zero-trust access for cloud-native and multi-cloud environments, including identity-tier and network-tier policies and application or service identity controls. A cloud-hosted system is not automatically zero trust.
- Use centralized identity where practical, strong authentication, and least privilege.
- Manage the full lifecycle of human and workload identities, including provisioning, changes, and removal.
- Review privileged access regularly and limit standing administrative permissions where the architecture allows.
- Include third-party access, service identities, applications, and machine-to-machine connections in access reviews.
- Select authentication methods for compatibility with the identity provider, phishing resistance, administrative scale, recovery, and user needs.
CISA identifies multifactor authentication as a security measure in its federal cybersecurity direction. Enterprises should adapt guidance developed for federal contexts to their own systems, contracts, threat models, and applicable requirements.
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Access controls work best when teams can detect changes and investigate what happened. Define approved configuration baselines, detect drift from them, and automate repeatable checks where practical. Centralize logs and alerts so investigators can correlate activity across cloud providers and on-premises systems.
CISA’s cloud architecture materials describe capabilities such as cloud security posture management, continuous monitoring, alerting, identity and access management, and risk assessment. NIST’s zero-trust guidance also emphasizes monitoring resource status and events such as access requests and directory changes. Choose coverage that fits the services in use, and establish who reviews alerts and how findings reach incident responders.
Useful operational questions include whether important accounts and services produce usable audit logs, whether configuration changes can be traced to an identity, and whether alerts are routed to a team with authority to act. Logging without ownership, retention, and response procedures is not a complete monitoring capability.
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Protect data and test recovery
Map sensitive data flows and apply access restrictions, encryption, and key-management practices appropriate to the environment. Confirm which protections the provider supplies and which the enterprise must configure or operate. Data controls should cover relevant stores, integrations, and copies—not only the primary application.
Recovery planning should protect backups from unauthorized modification or deletion and test restoration against the organization’s recovery objectives. CISA recommends frequent backups, enabled logging and alerts, and deletion protections such as object lock where supported. The appropriate backup design and retention depend on the cloud service and business needs; no single configuration is prescribed for every enterprise.
A backup that has never been restored in a representative exercise is an assumption, not demonstrated recovery capability. Record test outcomes, unresolved obstacles, and the owner responsible for addressing them.
Manage AI risk across its lifecycle
NIST AI RMF 1.0 organizes voluntary risk-management work into four functions: Govern, Map, Measure, and Manage. Together, they help an organization assign responsibility, understand context and foreseeable impacts, evaluate behavior and controls, and prioritize responses. They are not a certification that an AI system is safe.
- Govern: assign decision rights and responsibilities, and set policies for how AI risks are handled.
- Map: document the system’s purpose, users, context, inputs, dependencies, and potential effects.
- Measure: evaluate relevant system behavior, risks, and safeguards using methods suitable for that use case.
- Manage: prioritize and address risks, monitor the system, and revisit decisions as conditions change.
Include cybersecurity and privacy in design, deployment, operation, and evaluation. Depending on the system, assess data exposure, access to model endpoints, permissions granted to integrations, third-party dependencies, and monitoring needs. The relevant questions vary with purpose and deployment; an AI inventory entry alone is not a risk assessment.
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- Passwordless Login with PinPlex – Supports secure passkey login via WebAuthn and CTAP2 with added protection from PinPlex, a complex PIN system that enhances physical security.
- Multi-Layer Authentication Support – Includes PIV certificates and supports both TOTP and HOTP for strong 2FA/MFA coverage across enterprise and consumer apps.
As of October 2026, NIST indicates that AI RMF 1.0 is under revision. Its page also records an April 7, 2026 concept note for a critical-infrastructure profile; a concept note is not a final standard. Organizations using the framework should check NIST’s current status information rather than treat the 2023 framework text as permanently fixed.
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Pair technical indicators with impact, ownership, treatment, and trend. For example, privileged-access exposure matters more when leaders can see which critical service is affected, who owns the remediation, and whether the exposure is being reduced. The following are useful categories to tailor—not universal benchmarks or proof of security:
| Indicator | Business context to report |
|---|---|
| Privileged-access exposure | Critical services or data affected, accountable owner, and planned treatment. |
| Unresolved critical findings | Business impact, exposure window, assigned remediation owner, and status trend. |
| Logging coverage | Which important services and identities are observable, and where investigation gaps remain. |
| Recovery test outcomes | Which services were restored, obstacles found, and whether results meet business recovery objectives. |
| High-risk AI inventory | Systems requiring attention, their owners and lifecycle stage, and the status of risk treatment. |
Use consistent definitions and explain what each measure leaves out. NIST’s ERM guidance supports communicating cybersecurity risk across organizational levels; it does not establish an industry-wide target for these indicators.
Tailor the program to the architecture
There is no single cloud or AI security architecture that fits every enterprise. Before selecting controls or tools, compare the operating burden and integration requirements against the organization’s services, staffing, threat model, and risk tolerance. Use questions such as these to expose gaps:
- Service model and responsibility: Which tasks fall to the provider, and which does the enterprise configure, monitor, and recover?
- Identity coverage: Are workforce users, administrators, service identities, workloads, applications, and third parties included?
- Visibility: Can teams correlate audit logs, configuration drift, alerts, and investigations across providers and on-premises systems?
- Data protection and recovery: Are access controls, key management, backup isolation, deletion protections, and restoration demonstrated for the relevant services?
- AI lifecycle governance: Are ownership, context, security and privacy evaluation, monitoring, and treatment addressed as systems evolve?
- Operating burden: Do staffing, provider-specific tooling, automation, and integration allow findings to reach the enterprise risk owner and prompt action?
Frameworks are useful for organizing this work and communicating decisions. They do not substitute for tailoring controls to architecture, threats, legal obligations, and the organization’s tolerance for risk.
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