Application Governance
Does the system behave — and can we prove it?
The governance of AI systems, not just models: grounding and citation discipline, prompts and configurations under change control, output validation and egress handling, application identity and entitlements, release gates with safety regression, and scope fences that decide what the system may not do. The unit of validation moved from the model to the pipeline — retrieval, prompt, model, tools, and UI together.
Your customers, courts, and regulators judge the application's words — not the model card behind them.
Why leadership should care
- Courts hold companies liable for what their AI applications say: Air Canada lost on a policy its chatbot invented; NYC's business bot dispensed illegal advice under official branding for months.
- A one-line prompt change is a production change to behavior — the Grok incident shipped extremist output for hours because a prompt edit skipped safety regression.
- Scope is the cheapest safety control: Wendy's fenced its drive-thru AI to the menu with confidence-based human handoff and hit 86% autonomous completion at ~99% accuracy with assists.
- Which decisions may AI applications make alone, and which require a human at the point of consequence?
- Do prompts and AI configs go through the same change control as code? (They are code.)
- What may our customer-facing AI never discuss — price, contract, advice — and where is that enforced?
Grounding with Google Search & your data
GAGemini Enterprise Agent Platform
Citation-bearing answers grounded in Search or approved corpora.
Hallucination reduction with attributable, checkable sources.
Agent Search (formerly Vertex AI Search)
GAGemini Enterprise Agent Platform
Enterprise RAG with ACL-aware retrieval and grounded generation.
Answers constrained to approved documents, honoring existing permissions.
Gen AI evaluation service
GAGemini Enterprise Agent Platform
Computation and LLM-as-judge metrics for models, apps, and agents.
Documented pre-deployment quality gates and regression evidence.
Gemini safety filters
GAGemini models
Configurable harm-category thresholds plus non-configurable core filters.
Baseline content safety per request, tunable per use case.
Model Armor
GASecurity
Model-agnostic screening of prompts and responses, with org-wide floor settings.
Prompt injection, jailbreaks, sensitive-data leakage, and unsafe content — one policy over any model.
Apigee as AI gateway
GAApigee
Token quotas, semantic caching, model routing, Model Armor policies inline.
One governed front door for every model consumer — including non-Google models.
IAM & workload identity
GAIdentity
Least-privilege roles for models, endpoints, tuning, and agents.
Who may build, deploy, call, and administer AI — enforced, not documented.
Cards link to official documentation. Status is a snapshot (August 2026) — verify per component before contractual commitments. Full mapping and honest gaps: 08 · Google Cloud.
The model is 20% of the system; govern the pipeline that wraps it — that's where incidents actually start.
- Deutsche Bank publishes its eval regime — citation precision and recall, false-rejection rates, git-pinned baselines. That's what audit-ready gen AI concretely looks like.
- Treat prompts as production code. Half of the public AI incidents trace to an untested prompt or config change.
- The strongest deployments constrain scope first: menu-only, policy-corpus-only, intent-extraction-only. Capability follows trust.
- Ask where output validation happens. If the answer is 'the model is aligned', there is no application governance.
- For your most visible AI application: what may it never say or do, and where is that enforced?
- What happens between a prompt edit and production today?
- Can users and reviewers see sources for AI answers in high-stakes flows?
- How do you sample and review production AI conversations for quality?
- Who owns an AI behavior incident at 2am, and what is the kill/fix path?
Wells Fargo
Google CloudFinancial Services
You don't need to trust the model with PII to use the model — that's an architecture decision.
Deutsche Bank
Google CloudFinancial Services
'Audit-ready gen AI' concretely means: metric dictionary, versioned test sets, pinned baselines, logged prompts.
Commerzbank
Google CloudFinancial Services
Use a second service to judge and explain the model's work — the explanation becomes the audit trail.