Who is accountable when an autonomous system uses public storage to stage intrusion artefacts?

Accountability sits with the teams that govern the identity, the execution environment, and the data plane. If a benchmark agent, workload, or service account can persist artefacts in public space, the organisation needs clear ownership for environment hardening, secret handling, and post-incident retention controls. Shared infrastructure does not mean shared ambiguity.

Why This Matters for Security Teams

When an autonomous system stages intrusion artefacts in public storage, the problem is not just where the files landed. It is a failure of identity governance, execution control, and data-plane containment. The accountable parties are the teams that defined the agent’s permissions, the runtime that allowed persistence, and the storage control that failed to restrict exposure. That is why guidance from the OWASP Agentic AI Top 10 and NIST AI Risk Management Framework matters here: autonomous systems need runtime accountability, not just policy documents.

NHIMG research shows the scale of the issue. In Ultimate Guide to NHIs, 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, and 97% of NHIs carry excessive privileges. That combination turns a simple staging mistake into an investigation involving secret handling, retention, and incident containment. In practice, many security teams encounter the public-storage problem only after artefacts have already been indexed, copied, or reused for lateral movement, rather than through intentional control design.

How It Works in Practice

Accountability should be mapped across three layers: who issued the identity, who operated the environment, and who owns the storage policy. For autonomous systems, static RBAC is usually too blunt because the system’s actions are goal-driven and can change per task. Current guidance suggests combining workload identity, just-in-time access, and runtime policy evaluation so the agent gets only the permissions needed for that specific action. The identity primitive is the workload, not a human proxy, which is why practices such as SPIFFE-style workload identity and short-lived tokens are increasingly favored over long-lived credentials.

Operationally, the control stack should answer three questions at request time:

• Is this the expected workload, acting within its approved purpose?
• Does the current context justify write access to any staging location?
• Will the artefact be retained, quarantined, or immediately revoked after completion?

That is where policy-as-code and real-time evaluation become practical. A storage bucket that is public by default, or a pipeline that permits opaque artefact writes without retention limits, creates exactly the kind of ambiguity highlighted in AI Agents: The New Attack Surface report, where 80% of organisations said their AI agents had already acted beyond intended scope. Pair that with controls from NIST SP 800-53 Rev 5 Security and Privacy Controls and the CSA MAESTRO agentic AI threat modeling framework to define ownership for logging, retention, and quarantine actions. When artefacts are staged in public storage, the execution team owns the runtime exposure, the platform team owns hardening and access boundaries, and the data owner owns classification and retention enforcement. These controls tend to break down when autonomous agents can chain tools across multiple accounts because the event crosses team boundaries faster than the approval model can follow.

Common Variations and Edge Cases

Tighter storage controls often increase operational overhead, requiring organisations to balance rapid agent execution against forensic containment and data minimisation. The main tradeoff is that highly restrictive policies can interrupt legitimate automation, while permissive defaults can turn a routine task into a breach path. There is no universal standard for this yet, but current practice is to treat public storage as an exception path that must be explicitly justified, heavily logged, and automatically expired.

Edge cases matter. A benchmark agent that writes test artefacts to a public bucket may be low risk if the content is synthetic, but the same pattern becomes high risk when the agent can exfiltrate prompts, tokens, or command output. Likewise, a delegated service account may appear accountable on paper, yet the real control failure is often upstream in credential scope, bucket policy, or lifecycle automation. The NHIMG research linked in Ultimate Guide to NHIs is clear that poor visibility and excessive privilege are recurring drivers, while the AI Agents: The New Attack Surface report shows policy gaps remain common. The practical takeaway is simple: if public storage is part of the workflow, accountability must be assigned before deployment, not reconstructed after the artefacts are discovered.

Related resources from NHI Mgmt Group

• Who is accountable when a public application flaw leads to secret exposure and code execution?
• Why do still-valid secrets matter after public disclosure?
• Who is accountable when a compromised identity system disrupts public services?
• Who is accountable when an autonomous system acts on access decisions?