DNAi SaaS & Technology Systems Architect Certification

Establishes the architect-tier blueprint for AI across a multi-product SaaS company: a layered platform with shared services, trust-separated planes, and a model lifecycle that scales from one feature to the whole portfolio. Sets the structural decisions that operator-tier deployments quietly depend on.

• From Point Features to a Shared AI Platform
• Control Plane, Data Plane, Inference Plane
• The Enterprise Model Lifecycle and Vendor Exit

Hardens the AI platform against the threats unique to generative systems in multi-tenant SaaS: prompt injection through user content, cross-tenant leakage, training-data and retrieval exfiltration, and the privacy obligations that ride on every customer's data. Moves from operator-tier guardrails to enforceable, provable boundaries.

• Prompt Injection as a First-Class Threat
• Multi-Tenant Isolation Across Retrieval, Memory, and Cache
• Privacy Boundaries: PII, Retention, and Training

Builds the regulatory-grade governance layer the platform needs to sell into regulated buyers and survive audits: a model and risk register, policy tiers tied to enforcement, audit trails that reconstruct any decision, and the compliance posture (SOC 2, GDPR, the EU AI Act, sectoral rules) that AI features must inherit rather than reinvent.

• The Model Register and Risk-Tiered Policy
• Audit Trails That Reconstruct Any Decision
• Mapping to Frameworks and Third-Party AI Risk

Confronts the reality that enterprise AI lands on top of legacy systems, fragmented data, and a dozen SaaS tools that must interoperate. Covers integrating AI with systems of record without corrupting them, the build-buy-embed decision across the stack, agent orchestration across services, and migration strategy that does not require a rewrite.

• AI on Top of Systems of Record
• Build, Buy, or Embed Across the Stack
• Orchestrating Agents Across Services

Turns the AI platform into a system that stays up, stays affordable, and stays correct as it scales: SLOs and graceful degradation for non-deterministic components, continuous evaluation against drift, cost as a first-class architectural constraint, and the incident and capacity discipline of running generative systems in production.

• SLOs and Graceful Degradation for Non-Deterministic Systems
• Continuous Evaluation and Drift Detection
• Cost, Latency, and Capacity as Architecture