AI security for cloud operations teams in DACH

Prompt injection on ops agents is an access-control problem

Prompt injection is untrusted content instructing your agent to ignore policy: a ticket body, a log line, a pasted customer email, or a malicious README in a repo the agent reads. For an internal chatbot that only drafts text for a human, the impact is embarrassment. For an agent wired to Terraform plans, Kubernetes changes or ticketing with elevated rights, it is indistinguishable from a confused insider with your keys.

Defence is layered and boring in the good sense: treat every external or user-supplied string as hostile input; keep high-impact tools behind human approval or separate, narrow roles; log tool invocations with the prompt hash and caller identity; and never give an agent a superset of what the on-call engineer already had. Red-team exercises against your own runbooks - 'what if this alert description said ignore safeguards' - beat buying a dashboard that labels prompts 'risky' without changing IAM.

Tool and IAM least privilege for agents

An agent is a new principal in your identity model. If it shares a broad automation role used elsewhere, you cannot tell which action was the model, which was a script, and which was an attacker steering the model. The pattern that survives audit interviews is one role per agent workflow, scoped to the APIs and resources that workflow needs, with session or token lifetime short enough that a runaway loop burns out before damage spreads.

Map tools the way you map break-glass: each callable function is an API with its own risk class. Read-only diagnostics first; mutating changes only through guarded paths (plan-only, approval queue, or change window). Rotate credentials the agent uses independently of human SSO, and revoke them when the workflow is retired. ISO 27001 access control (A.5.15, A.8.2) and SOC 2 logical access themes apply to service principals exactly as they apply to people.

• ✓Separate roles for 'summarise incident' vs 'apply remediation' - never one super-role because it is easier to wire.
• ✓Deny by default on data plane mutations; allow lists per environment (dev may auto-fix; prod may not).
• ✓Attribute every tool call in CloudTrail, Azure Activity Log or GCP Audit Logs with the agent's identity, not a shared lambda admin.

Secrets in context windows and subprocessors

Ops agents tempt teams to paste connection strings, kubeconfigs, token dumps and customer data into prompts 'just this once'. Context windows are not vaults: they may be logged by the vendor, retained for abuse monitoring, used in fine-tuning only if contract forbids it - and they replicate across traces and support tickets. GDPR and your customer DPAs still apply when the payload is a log excerpt with personal data.

Engineering should route secrets through references, not literals: fetch from a secrets manager at execution time with a scoped identity, redact before the model sees text, and block patterns that look like keys in outbound prompts. Subprocessor due diligence for the model provider belongs in the same register as your observability vendor: region, retention, training opt-out, and whether support staff can read prompts. None of that is solved by a larger context window.

Logging and evidence for SOC 2, ISO 27001 and DORA

Auditors and supervisors ask for reconstructable timelines: who invoked the agent, what tools ran, what changed in the estate, and whether logs are tamper-evident. DORA Articles 17-23 expect major-incident records with defensible timestamps; ISO 27001 expects protection and review of logs (A.8.15, A.8.16); SOC 2 expects monitoring and logging appropriate to risk. An agent without structured audit trails is a gap you discover in the first sample request.

Minimum viable evidence for ops AI: immutable or WORM-capable storage for security-relevant agent logs, correlation IDs linking chat session to CloudTrail events, retention aligned to your ISMS (not the SaaS default), and runbooks that name the queries to pull a timeline from archive. If the only record lives in a vendor UI with a thirty-day retention, you do not have evidence - you have convenience.

EU AI Act literacy overlaps with how you already run cloud

Most internal ops AI is minimal or limited risk under the AI Act, but horizontal duties still apply: AI literacy (Article 4), honest inventory with classification reasoning, and transparency where users interact with AI (Article 50). That overlaps cleanly with cloud governance you should already have - access reviews, vendor management, change control - if you treat agents as production systems rather than experiments.

High-risk Annex III domains are rare in pure ops tooling, but border cases exist when AI influences employment, credit or critical infrastructure decisions. Write one-line classifications while designs are fresh; involve legal when a workflow touches regulated outcomes. Literacy is not a yearly e-learning checkbox - it is ensuring on-call engineers know an agent can be injected, that approvals exist, and where logs live.

What Stratoworks implements - controls, not AI security SaaS

The market offers AI firewalls, prompt filters and governance platforms. Some are useful as layers; none replace IAM, logging, secrets hygiene and runbooks on your cloud accounts. Stratoworks does not sell those products. We design agent boundaries in your landing zone: scoped roles in Terraform, export of agent and platform logs to customer-controlled EU storage, approval gates for mutating tools, and integration with your existing ISMS and incident process.

If you already have a copilot or agent framework chosen, we harden the estate it runs against - least privilege, evidence paths, redaction patterns, and tabletop scenarios that include prompt injection. The outcome is ops AI your team can run and your compliance function can sample, without pretending a separate AI-security subscription equals a certified control.

FAQ