Why AI workloads get held at the gate
AI projects rarely stall on model quality. They stall because nobody can say what data the model reaches, which identity it runs as, or what the SOC would see if it were abused.
No inventory. Teams stand up Vertex AI endpoints, notebooks, vector stores and third-party model keys faster than security can catalogue them. You cannot approve what you cannot see.
Over-permissioned agent identities. An agent that can call tools is an identity with a blast radius. Many pilots ship with a broad service account, so a prompt injection becomes a lateral movement path rather than a bad answer.
Sensitive data in the context window. Retrieval pipelines inherit whatever the source bucket or index holds. Without classification and access boundaries, retrieval becomes a route to records the requester was never entitled to see.
No detections for AI. Even with clean posture, the runtime question stands: if this agent is abused at 2am, does anyone see it? Most SIEMs have no AI telemetry, no detection content and no playbook for it.
Two halves of one control loop
Wiz secures the path into production. Google SecOps covers what happens once you are there.
Before production: Wiz
Wiz connects agentlessly across your Google Cloud organisation and any AWS or Azure estate, and builds a graph of what is actually exposed. For AI, it finds the model services, training data, notebooks and pipelines your teams have deployed, and shows where a misconfiguration chains into an attack path.
- AI inventory across Vertex AI, managed services and self-hosted models
- Toxic combinations: public exposure plus critical CVE plus excessive permissions
- Sensitive training and retrieval data located and classified
- CI/CD guardrails that block insecure images and Terraform before merge
More on Wiz deployment and remediation.
In production: Google SecOps
Google SecOps ingests your AI telemetry alongside the rest of the estate, normalises it into the Unified Data Model and runs detections against it continuously. An abused agent becomes an alert with an owner and a playbook.
- Vertex AI, audit, IAM and application logs in one timeline
- Custom YARA-L detections for unusual agent and service account behaviour
- Mandiant threat intelligence applied to entities in your environment
- SOAR playbooks that revoke tokens and quarantine workloads
More on Google SecOps implementation.
How the loop works
- Discover. Wiz maps every AI asset, the data behind it and the identities attached to it.
- Remediate. We fix the attack paths that block sign-off, such as exposure, permissions and unclassified data, with Terraform guardrails so they stay fixed.
- Instrument. Wiz findings and AI telemetry flow into Google SecOps, so graph context sits next to runtime events in one investigation.
- Operate. Our agentic SOC triages what fires, enriches it with the Wiz graph and runs pre-approved response actions around the clock.
A risk committee approves an AI workload when two things are true: the known risks are closed, and the unknown ones would be caught. Wiz evidences the first and Google SecOps the second. Neither is enough on its own.
Run by our agentic SOC
AI workloads add alert volume to a SOC that is often already stretched. Our Managed Agentic SOC uses Gemini based agents to correlate alerts, pull entity history and write up the incident before an analyst opens it. The same team runs your Wiz tenant and your Google SecOps instance, so posture and runtime are not two vendors pointing at each other during an incident.
- Triage: agents correlate signals across Wiz and Google SecOps and close noise with a recorded rationale.
- Investigation: each alert arrives with asset exposure, permissions and data sensitivity attached.
- Response: token revocation, workload isolation and IP blocking run to playbooks your team approved in advance.
- People: Google Cloud security engineers, with clear escalation into your own team.
The AI risks we cover
| Risk | What we do |
|---|---|
| Shadow AI | Find unsanctioned model endpoints, notebooks and API keys and bring them under policy |
| Prompt injection | Screen at the model boundary and limit tool access, so a successful injection has nowhere to go |
| Training and retrieval data leakage | Discover sensitive data across buckets, BigQuery and vector stores, and enforce access boundaries |
| Agent identity sprawl | Scope down service accounts and workload identities, and detect unusual use at runtime |
| Supply chain and model provenance | Scan images, libraries and model artefacts before they reach production |
| Regulatory evidence | Continuous control evidence for APRA CPS 234, ISO 27001, SOC 2 and the ASD Essential Eight |
Where to start
Book an AI readiness review. We look at what you are trying to ship, what is blocking it, and what Wiz and Google SecOps would need to cover for your risk team to say yes.
Questions we get asked
Do we need both Wiz and Google SecOps?
They solve different problems. Wiz tells you what is exposed and how an attacker would chain it together, but it does not watch your logs in real time. Google SecOps tells you what is happening now across your telemetry, but it does not map your cloud attack surface. Organisations blocked on AI sign-off are usually missing one of the two, and adding the second is what unblocks the review.
We already have a SIEM. Does this mean replacing it?
Not necessarily. Google SecOps can run alongside an existing SIEM, starting with the cloud and AI telemetry where per-gigabyte ingestion pricing hurts most, then reviewing consolidation once the value is proven.
Does this only apply to Google Cloud workloads?
No. Wiz connects agentlessly to AWS and Azure as well, and Google SecOps ingests telemetry from those platforms alongside Workspace, Okta and endpoint tooling. Google Cloud is where our engineering depth sits, but coverage is not limited to it.
Can you work with our existing security team?
Yes. Engagements range from a deployment and detection engineering project handed over to your team, through to a fully managed agentic SOC. Most clients start with a scoped assessment and decide the operating model from there.
What does an engagement start with?
An AI readiness review. We inventory the AI workloads you have, identify what is blocking production sign-off, and return a prioritised remediation and detection plan. Scope and duration depend on the size of your estate, and we confirm both before starting.
Talk to an architect who has done this before.
Bring your current setup and the outcome you need. You will get a view on the approach, the risks and roughly what it costs.
Straight to a senior GCP architect. No SDR, no slide deck.
Not ready to talk? See how we migrated Hapana off AWS →
Or call +61 2 8359 9507 · Hello@aviato.consulting