Your AI Pilot Works. Security Still Will Not Sign It Off.
Most AI workloads do not stall on model quality. They stall at the security gate, because nobody can answer what data the model reaches, which identity it runs as, or what the SOC would see if it were abused. Wiz answers the first question. Google SecOps answers the second. Together they close the gap that keeps AI stuck in pre-production.
Why AI Workloads Get Held at the Gate
The blockers are rarely about the model. They are about everything around it.
Nobody Has an Inventory
Teams stand up Vertex AI endpoints, notebooks, vector stores, and third-party model keys faster than the security team can catalogue them. You cannot approve what you cannot see, and shadow AI is now the default state.
Over-Permissioned Agent Identities
An agent that can call tools is an identity with blast radius. Many pilots ship with a broad service account attached, so a prompt injection becomes a lateral movement path rather than a bad answer.
Sensitive Data Reaches the Context Window
Retrieval pipelines inherit whatever the source bucket or index holds. Without data classification and access boundaries, the retrieval layer quietly becomes an exfiltration route for records the requester was never entitled to.
The SOC Has No Detections for AI
Even where posture is clean, 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 an agent gone wrong.
Two Halves of One Control Loop
Wiz secures the path into production. Google SecOps secures what happens once it is there.
Wiz: Posture, Attack Paths, and AI Inventory
Wiz connects agentlessly across your Google Cloud organisation and any AWS or Azure estate, then builds a graph of what is actually exposed. For AI specifically, it discovers the model services, training data, notebooks, and pipelines your teams have deployed, and shows where a misconfiguration chains into a real attack path.
- →Full 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
Google SecOps: Detection and Response
Google SecOps ingests your AI telemetry alongside the rest of the estate at petabyte scale, normalises it into the Unified Data Model, and runs detection content against it continuously. This is where an abused agent stops being invisible and becomes an alert with an owner and a playbook.
- →Vertex AI, audit, IAM, and application logs ingested into one timeline
- →Custom YARA-L detections for anomalous agent and service account behaviour
- →Mandiant threat intelligence applied to entities in your environment
- →SOAR playbooks that revoke tokens and quarantine workloads automatically
How the Two Products Close the Loop
Posture findings become detection content. Runtime signals become posture priorities.
Discover
Wiz maps every AI asset, the data behind it, and the identities attached to it. You get a defensible inventory instead of an assumption.
Remediate
We fix the attack paths that block sign-off: exposure, permissions, and unclassified data, with Terraform guardrails so they stay fixed.
Instrument
Wiz findings and AI telemetry are forwarded into Google SecOps, so graph context sits next to runtime events in a single investigation.
Operate
Our agentic SOC triages what fires, enriches it with the Wiz graph, and executes 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. Google SecOps evidences the second. Neither is sufficient alone, which is why single-product AI security programmes tend to stall at the review.
Operated by Our Agentic SOC
Buying the tools is the easy part. Running them is where the value is.
AI Defending AI
Adding AI workloads to your estate adds alert volume to a SOC that is already saturated. Our Managed Agentic SOC uses Gemini-driven agents to correlate alerts, pull entity history, and write the threat narrative before a human opens the ticket, so analyst time goes to decisions rather than triage.
The same team runs your Wiz tenant and your Google SecOps instance, which means posture and runtime are not two vendors pointing at each other during an incident.
Autonomous Triage
Agents correlate related signals across Wiz and Google SecOps and dismiss the noise with a recorded rationale.
Graph-Enriched Investigation
Every alert arrives with the asset exposure, permissions, and data sensitivity already attached.
Pre-Approved Response
Token revocation, workload isolation, and IP blocking run to playbooks your team signed off in advance.
Certified Google Cloud Engineers
A specialist Google Cloud security team, with clear escalation paths into your own people.
The AI Risks We Cover
Mapped to the questions your risk and compliance teams will actually ask.
Shadow AI
Unsanctioned model endpoints, notebooks, and third-party API keys discovered and brought under policy.
Prompt Injection Impact
Screening at the model boundary, plus least-privilege tool access so a successful injection has nowhere to go.
Training and Retrieval Data Leakage
Sensitive data discovery across buckets, BigQuery, and vector stores, with access boundaries enforced.
Agent Identity Sprawl
Service accounts and workload identities scoped down, with anomalous use detected at runtime.
Supply Chain and Model Provenance
Container images, libraries, and model artefacts scanned before they reach a production endpoint.
Regulatory Evidence
Continuous control evidence aligned to APRA CPS 234, ISO 27001, SOC 2, and the ASD Essential Eight.
Frequently Asked Questions
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 right 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 incumbent 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?
Wiz connects agentlessly to AWS and Azure as well, and Google SecOps ingests telemetry from those platforms alongside Workspace, Okta, and endpoint tooling. Aviato is a Google Cloud specialist, so Google Cloud is where our engineering depth sits, but the 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.
Get Your AI Workloads Into Production
Book a review with our security engineers. We will 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.
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