Tenant isolation
Separate runtime state, workspace, credentials, sessions, logs, and operational access by tenant instead of merging agents into shared identities.
Security and Governance
Secure AI agents by putting every runtime inside a governed tenant boundary
AI agent security and governance for enterprise teams means defining who owns each runtime, which secrets and policies it can use, how access can be blocked, where browser work happens, and what evidence operators can inspect. Bewize supports that model through Bewize Hub tenant isolation, managed secrets, policy APIs, storage boundaries, browser sidecar isolation, redacted metrics, and evaluation evidence. These are named operating controls, not a blanket security or compliance guarantee.
Answer
What should enterprise AI agent security control?
Enterprise AI agent security should control tenant identity, runtime state, managed credentials, environment keys, policy boundaries, access state, browser isolation, storage, run history, and redacted operational metrics before agents handle real work. In Bewize, those controls map to Bewize Hub tenant records, per-tenant runtime isolation, one Unix user per tenant in production provisioning, managed secret metadata, tenant environment secret APIs, agent policy APIs, access blocking, restricted rsync access, browser sidecar boundaries, and evaluation evidence.
Managed secrets
Keep secret metadata and environment keys under central operational control while avoiding UI surfaces that render raw secret values back to operators.
Policy APIs
Control available capabilities, runtime behavior, and tenant-specific feature boundaries from the operating layer.
Access blocking
Give operations a direct control for blocking tenant access and recovering the runtime boundary when a tenant should stop running work.
Claim boundary
Bewize names concrete controls and evidence. It does not claim certifications, absolute security, or compliance guarantees it cannot back with source.
Security questions to answer before scaling agents
Use governance as the connective layer between deployment, orchestration, observability, browser work, and evaluation.
Where do the runtimes live?
Decide the private host, storage, release, and network boundary before agents start production work.
Review →How are agents orchestrated?
Connect security controls to lifecycle, schedules, releases, run evidence, and stop paths.
Review →How are secrets managed?
Separate secret metadata, refresh controls, tenant environment keys, and raw secret values.
Review →Where does browser work happen?
Keep employee and agent web work inside a controlled browser sidecar boundary when browser automation is required.
Review →What evidence is available?
Review run history, redacted usage, schedules, request counts, and runtime-impact signals instead of relying on promises.
Review →How will quality be checked?
Pair governance with evaluation evidence before widening what agents can do.
Review →Secret metadata without secret exposure
Bewize Hub manages tenant OAuth secret metadata and environment keys through its API-first control plane and focused operator console. This description is limited to source-backed API and runtime behavior.
Managed secret metadata
The UI shows name, scope, provider, status, next refresh, and refresh/delete controls.
Tenant env keys
Operators can write tenant environment keys while the stored value is not rendered back into the UI.
Tenant scope
Secrets and environment keys are shown against the selected tenant boundary.
Redaction proof
Source-backed checks keep raw secret values out of returned API metadata.
Operational outcome
Operations can manage tenant credentials without introducing a graphical admin surface where secret values are casually copied.
AI agent security FAQ
What is AI agent security for enterprise teams?
AI agent security means controlling the runtime boundary: tenant identity, credentials, policy, access state, storage, browser behavior, run evidence, and redacted metrics. Bewize frames those as operating controls that enterprise teams can evaluate before giving agents real work.
Does Bewize claim a security certification?
No. Bewize offers concrete source-backed controls and evaluation evidence, not unsupported certifications or blanket compliance guarantees.
How do managed secrets work in this model?
Bewize Hub exposes secret metadata and refresh controls while keeping raw values out of returned UI metadata. Tenant environment keys are written against the tenant boundary and are not rendered back as casually copyable values.
What should a buyer evaluate first?
Start with tenant isolation, runtime ownership, policy boundaries, managed credentials, access blocking, browser isolation, run history, redacted metrics, and the evidence operators will review after each change.
Evaluate AI agent governance
Discuss the tenant boundaries, managed secrets, policies, access controls, browser isolation, run evidence, and claim boundaries your team needs before agents handle production work.
Architecture conversation
Tell us what your team needs to control
Share your deployment boundary, number of agents, work surfaces, and governance requirements. We will reply by email to arrange a focused technical discussion.
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