Building a Secure, Audit-Ready Infrastructure for an Enterprise AI Platform

About the client

A US-based AI and Behavioral Science SaaS Company

The client is a US-based AI-powered SaaS platform that simulates real-world consumer populations, enabling organizations to test strategies, predict behavioral outcomes, and make data-driven decisions using large-scale behavioral simulations. The platform is built on a distributed, Kubernetes-native architecture spanning AWS, GCP, and Azure, serving enterprise clients in research, CPG, and financial services sectors where data security and compliance are non-negotiable requirements.

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Problem Statement

An Expanding Platform with No Security Foundation and No Path to Enterprise Trust.

As the platform grew to serve enterprise clients across research, CPG, and financial services, the absence of a formal security foundation became a hard blocker to growth. The multi-cloud infrastructure had no centralized visibility, no standardized access controls, and no documentation that could hold up under audit scrutiny. Enterprise deals were being stalled by security questionnaires the team could not answer with confidence. The client needed more than a compliance checklist — they needed a secure, well-architected infrastructure built to earn and sustain enterprise trust. Pace Wisdom was engaged to design and deliver that foundation from the ground up.

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Challenge

Key security, compliance, and operational gaps identified across the client's infrastructure.

No Centralized Visibility Across Multi-Cloud Infrastructure
With workloads distributed across AWS, GCP, and Azure, there was no unified security monitoring layer. Each cloud operated in isolation with independent tooling, making it impossible to detect cross-environment threats, enforce consistent policies, or report compliance posture holistically.
Inconsistent Security Policies Across Services and Environments
Security configurations varied significantly between cloud providers, microservices, and deployment environments. The absence of standardized IAM policies, encryption baselines, and network security rules created multiple uncontrolled exposure points across the platform.
Insufficient Access Controls, Logging, and Monitoring
Gaps in role-based access controls, insufficient audit logging, and the absence of real-time alerting mechanisms meant that unauthorized access attempts and anomalous behaviors could go undetected. This directly conflicted with SOC 2 availability and security trust service criteria.
Weak Kubernetes Security Posture Across Distributed Workloads
The Kubernetes environment lacked hardened RBAC configurations, proper secrets management, and workload isolation. Container security was not enforced at the cluster level, exposing sensitive data processing pipelines to potential lateral movement and privilege escalation risks.
No Compliance Documentation or Audit Evidence
The platform had no structured documentation trail to support a SOC 2 audit. Control mapping, evidence collection, policy documentation, and audit logs were absent or scattered across teams, making compliance readiness assessment and remediation planning extremely difficult.

Solution

Pace Wisdom designed and built a secure,audit-ready infrastructure from the ground up — combining a structured SOC 2 gap assessment, multi-cloud security hardening, and a repeatable compliance framework that the client now owns and operates independently.

SOC 2 Gap Assessment and Control Mapping
Conducted a comprehensive assessment of the client's architecture against all five SOC 2 Trust Service Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy. Mapped existing controls to SOC 2 requirements using Scrut.io, identified gaps, prioritized remediation items by risk level, and created a structured compliance roadmap.
Standardized Security Policies Across Multi-Cloud Environments
Designed and implemented unified security baselines covering IAM policies, encryption at rest and intransit, network segmentation, and secrets management across AWS, GCP, andAzure. Enforced consistent configuration standards using infrastructure-as-code, eliminating environment-specific security drift.
Centralized Logging, Monitoring, and Alerting
Deployed a centralized observability stack aggregating security logs, access events, and system metrics from all three cloud environments into a single monitoring layer. Configured real-time alerting for anomalous activity, enabling proactive threat detection and providing the continuous monitoring evidence required for SOC 2.
Kubernetes Security Hardening
Implemented hardened RBAC policies, enforced workload isolation using namespace-level controls, integrated secure secrets management, applied pod security standards, and conducted container image vulnerability scanning. Reduced the Kubernetes attack surface and aligned cluster security with SOC 2 processing integrity requirements.
Compliance Documentation and Audit Trail Establishment
Built structured policy documentation, evidence collection workflows, and audit-ready reporting aligned to SOC 2 control requirements. Established repeatable processes for ongoing compliance evidence gathering, ensuring the client could sustain compliance posture beyond the initial audit cycle.

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No items found.

Technology used

Built with AWS, Docker, and Kubernetes for scalable cloud infrastructure, seamless deployment, and high performance.

Impact

Measurable outcomes from building a secure, enterprise-grade infrastructure for a multi-cloud AI platform.

100%

SOC 2 Audit Ready

3x

Cloud Environments Secured

60%

Reduction in Security Gaps

"Pace Wisdom brought structure to what felt like an overwhelming compliance challenge. They did not just assess gaps, they fixed them and built a system we can sustain."

CTO, AI Behavioral Simulation Platform

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