What should organisations do before connecting AI agents to sensitive BigQuery data?

Why This Matters for Security Teams

Connecting an AI agent to BigQuery is not just a data integration choice. It is a privilege decision that can expose regulated rows, restricted columns, and derived outputs to a workload that can change its own path at runtime. The core risk is not only exfiltration by the model, but also the agent chaining tools, re-querying data, or finding alternate retrieval paths after the first control has already failed. The OWASP NHI Top 10 and OWASP Agentic AI Top 10 both reflect this shift: the control question is no longer only who can log in, but what the agent can reach once it starts reasoning and acting. NHIMG’s own reporting on the AI agents attack surface shows how quickly scope creep becomes a real security issue in live deployments.

In practice, many security teams encounter the policy gap only after an agent has already seen data it should never have been able to request.

How It Works in Practice

Before an agent is connected, the security team should verify that BigQuery enforces restrictions at the platform layer, not in a downstream prompt, post-processing step, or application-side filter. For sensitive datasets, that means confirming row-level security, column-level security, dataset permissions, and service account scope are all aligned with the intended task. If the agent uses a query tool, the retrieval path must be constrained so it cannot simply broaden the query, ask for a different table, or call a separate export path to recreate the same exposure.

In agentic environments, static role-based access is often too blunt because the agent’s intent changes from one step to the next. Current guidance suggests treating the agent as a workload identity with narrow, short-lived access, then evaluating each request against policy in real time. That is consistent with the direction of the NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework, both of which emphasize governance, traceability, and threat-aware design. Practically, teams should:

• Use a dedicated workload identity for the agent, not a human shared account.
• Issue just-in-time credentials with tight TTLs and automatic revocation.
• Test that row and column controls survive every access path, including retries and alternate queries.
• Log the exact dataset, query, and returned fields for audit and incident response.
• Block any tool route that can bypass native BigQuery restrictions.

NHIMG research on the AI agents attack surface is clear that agent behaviour already exceeds intended scope in many organisations, which is why this pre-connection validation matters so much. These controls tend to break down when the agent can invoke multiple tools across separate services because each service may enforce policy correctly in isolation while the combined workflow still leaks sensitive data.

Common Variations and Edge Cases

Tighter pre-connection controls often increase setup effort, requiring organisations to balance faster agent onboarding against stronger data containment. That tradeoff becomes more visible when BigQuery is feeding summarisation, analytics, or retrieval-augmented workflows, because the agent may not need the full dataset to answer the business question. In those cases, the safer pattern is to expose a purpose-built view or controlled export rather than grant broad table access and hope the model behaves.

There is no universal standard for this yet, but current guidance suggests treating derived data as sensitive if the underlying rows or columns are sensitive. This matters because an agent can infer restricted facts even when the raw field is hidden. Teams should also watch for alternate paths such as cached copies, connected notebooks, data connectors, or downstream orchestration jobs that can reintroduce the same data outside BigQuery policy. For that reason, security review should include the full retrieval chain, not just the database permission set. The Ultimate Guide to NHIs - 2025 Outlook and Predictions is useful context here, because it reinforces that identity governance must match machine behaviour, not human assumptions.

Where the guidance breaks down most often is in environments with loosely governed BI tools or custom agent plugins, because a well-restricted warehouse can still be exposed through a less controlled adjacent system.

Related resources from NHI Mgmt Group

• What should organisations do before letting AI agents act on business data?
• How do organisations govern sensitive data in AI agents and LLM workflows?
• How should organizations approach the governance of AI agents?
• Should organisations enforce least privilege for AI agents before or after deployment?