Kunal Malhotra

Overview

The company was building a next-generation HR platform leveraging AI agents & large language models (LLMs) for candidate screening, interviews, & data analytics. Given the sensitivity of personal & health-related data processed by the platform, there was a need to ensure strong security controls, privacy safeguards, & readiness for external audits. Our engagement focused on aligning their security posture with globally recognized compliance frameworks, establishing trust with clients, & enabling a smooth certification process.

Challenge

• Complex AI Architecture: The use of AI agents & LLMs introduced unique risks such as data leakage, prompt injection, & model misuse, which were not fully addressed by traditional security controls.
• Regulatory Overlap: The platform needed to comply with multiple frameworks including SOC 2, ISO 27001, HIPAA, and GDPR, which required a unified and streamlined approach to control mapping.
• Evidence & Process Gaps: The team lacked structured documentation, policies, & monitoring mechanisms needed for audit readiness.
• Data Privacy Risks: Sensitive PII & PHI data flowed through multiple AI pipelines without clear data retention, anonymization, & access control mechanisms.

Approach & Methodology

A structured approach was followed to assess risks, implement controls, and prepare the platform for compliance and audit readiness.

• Discovery & Architecture Review: Conducted a full review of the platform architecture, including AI agents, LLM pipelines, and integrations with external services. All data flows, including PII, PHI, and sensitive HR data, were mapped to understand collection points, storage, processing, and transmission. System components, APIs, and trust boundaries were documented to identify potential security exposures.
• Control Gap Analysis: Performed a control-by-control assessment against SOC 2 Trust Service Criteria, ISO 27001 Annex A controls, HIPAA Security & Privacy Rules, and GDPR principles. Existing controls were recorded, gaps were identified, and priorities were assigned based on risk level and compliance urgency.
• Risk Assessment & Threat Modeling: Developed a risk register listing all identified threats, including AI-specific risks such as prompt injection, model poisoning, and data leakage. Likelihood and impact scores were assigned, overall risk was calculated, and mitigation strategies were defined.
• Policy & Process Development: Drafted policies covering access control, data retention, change management, incident response, and AI model governance. Processes for vendor onboarding, data minimization, and breach notification were defined to meet regulatory obligations.
• Control Implementation: Technical controls were deployed including encryption for data at rest and in transit, centralized logging and monitoring, role-based access control with least privilege, multifactor authentication, and automated vulnerability management. Audit logging for AI model interactions was configured and monitoring dashboards were established.
• Evidence Collection & Audit Playbook: Developed a compliance evidence repository with templates for policies, risk assessments, and system configurations. Log collection was automated where possible, and a playbook was created outlining the steps to prepare for SOC 2, ISO 27001, HIPAA, and GDPR audits, including required documentation and walkthrough sessions.
• Stakeholder Training & Awareness: Training sessions were conducted for developers, product managers, and leadership teams, covering secure coding for AI workflows, data privacy principles, incident reporting procedures, and audit expectations.

Deliverables

• Architecture Risk Assessment Report: Provided a detailed analysis of AI workflows, data flows, and security gaps, along with recommended mitigation strategies.
• Compliance Gap Analysis: Delivered a control-by-control mapping against SOC 2, ISO 27001, HIPAA, and GDPR, highlighting gaps and prioritized remediation steps.
• Security Policies & Procedures: Produced a comprehensive set of policies aligned with best practices and regulatory requirements.
• Audit Readiness Playbook: Created step-by-step guidance to streamline evidence submission and support external auditor interactions.
• Risk Register & Treatment Plan: Compiled all identified risks with severity ratings, mitigation strategies, and assigned owners for ongoing governance.

Outcome

The engagement resulted in a robust security and compliance program for the AI-driven HR platform. SOC 2 Type 1 readiness was achieved, a governance model for ISO 27001 certification was established, and privacy controls for HIPAA and GDPR were implemented. A repeatable audit preparation process was created, enabling efficient evidence collection and demonstrating compliance maturity. This strengthened platform security, reduced regulatory risk, and enhanced trust with enterprise clients.