Knowledge Base
Answers to common questions about implementing AI with trust, safety, and compliance built in.
What's the difference between AI compliance and AI governance?
Compliance is meeting external requirements—HIPAA, SOC 2, EU AI Act, contracts. Governance is your internal operating system: who approves model changes, how you handle incidents, what guardrails you enforce. Compliance is the floor. Governance is how you maintain trust at scale.
We already have a data governance program. Doesn't that cover AI?
How long does it take to implement AI trust infrastructure?
Where do most teams fail when implementing AI safety controls?
Is AI trust only relevant for regulated industries?
For mid-market B2B starting from basic cloud infrastructure:
2–4 months: Foundational controls (IAM, audit logging, basic guardrails)
6–12 months: Production-grade governance (red teaming, agent authorization, vendor risk management)
12–24 months: Compliance certification readiness (SOC 2, HIPAA attestation)
Timeline compresses with executive buy-in. It stretches without dedicated ownership.
No. Every B2B company selling into enterprise will face AI trust questions: How do you prevent prompt injection? What's your data retention policy? Do you log agent actions? If you can't answer with documentation, you lose on vendor risk assessment—regardless of industry. Trust is table stakes for enterprise buyers.
Not quite. Traditional data governance assumes static datasets and human-controlled queries. AI introduces dynamic behavior: models that hallucinate, agents that act autonomously, outputs that vary with identical inputs. You need AI-specific governance for model versioning, prompt management, guardrail enforcement, and real-time monitoring.
They treat safety as a pre-launch checklist instead of an operational discipline. The failure pattern: build the feature, bolt on guardrails at the end, get blocked in review. What works: threat modeling during design, guardrails as product requirements, compliance artifacts generated from actual architecture, post-deployment monitoring.
What's the difference between AI safety and AI security?
Safety is about what the model does—preventing harmful outputs, mitigating bias, ensuring factual grounding. Security is about protecting the system—preventing unauthorized access, defending against adversarial attacks, auditing changes. A safe model can be insecure (no access controls). A secure system can be unsafe (authorized users get bad advice). You need both.
Do we need AI governance if we're only using third-party AI vendors?
Yes. You're still accountable for how AI processes your data and affects your customers. You need vendor evaluation criteria, data processing agreements, integration security controls, and monitoring for model drift or policy violations. Using vendors doesn't eliminate governance—it shifts it to vendor management and integration oversight.
What's the ROI on AI governance infrastructure?
Compressed vendor evaluations (6 months → 6 weeks), faster internal approvals (eliminate 3–4 review cycles), access to regulated markets you currently avoid, and reduced incident response costs. The cost isn't the governance work—it's the AI projects that stall indefinitely without it.
When should we start building AI governance—before or after our first AI project?
Before, if possible. Building governance into your first AI project costs 20% more time upfront but saves 60% on every project after. Building it retroactively means re-architecting systems already in production, rewriting vendor contracts, and explaining to stakeholders why the "finished" project needs another 3 months of compliance work.