There is a question that more US and UK boards are asking before they approve GCC investment in 2026, and it is not the one you might expect. It is not “What is your AI strategy?” — that question was 2024. The question boards are asking now is: “If your GCC’s AI systems were audited tomorrow, what would they find?”
The honest answer for most GCCs is: more exposure than they realise. The EY GCC Pulse Report 2025 found that while 83% of GCCs are scaling GenAI projects and 58% are investing in Agentic AI, only 7% have established dedicated Centres of Excellence to manage AI risk. That gap — between AI deployment and AI governance — is where significant compliance and operational exposure lives.
This audit is designed for GCC operators, founding teams, and the US or UK parent company leaders who sponsor GCC investments. It covers 10 checkpoints across technology infrastructure, data governance, compliance architecture, and organisational readiness. Work through it honestly. The gaps you find now are far cheaper to close than the ones a regulator or an enterprise client finds later.
Technology Infrastructure
- Does your GCC’s infrastructure support AI workloads — or is it running AI on a stack built for something else?
AI workloads — particularly large model inference, real-time analytics, and agentic workflows — have materially different infrastructure requirements than traditional software delivery. GPU compute access, low-latency networking, high-throughput storage, and cloud architecture designed for AI pipelines are prerequisites, not nice-to-haves. GCCs that are running AI initiatives on infrastructure configured for a 2019 delivery model are creating performance bottlenecks and cost inefficiencies that compound at scale. Audit whether your infrastructure was designed for AI from the ground up, or whether AI has been layered onto an existing stack.
2. Are your AI systems and the data they depend on physically and logically separated from non-AI systems?
Data security for AI systems requires more than perimeter controls. Training data, model weights, inference logs, and output data all carry different risk profiles and often different regulatory requirements. A GCC that has not implemented logical separation between AI and non-AI data environments is creating a single point of failure where a breach in one system can compromise the integrity of all AI outputs — and the compliance of all data handling. This is particularly acute for GCCs in BFSI, healthcare, and fintech where data classification requirements are regulated.
AI systems expand the attack surface of a GCC in specific ways: model extraction attacks, adversarial input injection, training data poisoning, and prompt injection in LLM-based applications are all threat vectors that traditional perimeter security does not address. Zero Trust architecture — verifying every user, device, and request — is the baseline. But the specific configuration of that architecture needs to account for AI-specific threat vectors. If your security team cannot describe how your ZTNA deployment protects against prompt injection in your customer-facing AI applications, you have an unclosed gap.
Data Governance
4. Does your GCC have a documented data map that covers all data flowing into, through, and out of your AI systems — including cross-border transfers?
India’s Digital Personal Data Protection (DPDP) Act 2023 requires that personal data handled by GCCs is collected with documented consent, processed only for specified purposes, retained only for the required period, and protected against unauthorised access. For AI systems that ingest personal data as training input or inference input, every one of these requirements applies — and many GCCs have not mapped their AI data flows in sufficient detail to demonstrate compliance. Cross-border data transfers (when candidate data, customer data, or employee data moves to parent company systems in the US or UK) require specific safeguards that must be documented.
3. Have you established data retention and deletion policies that are actually enforced in your AI training pipelines?
DPDP sets specific retention limits for different categories of personal data. AI training pipelines often create data artefacts — cached datasets, intermediate outputs, model checkpoints — that are not covered by standard retention policies because no one mapped them as “data” at the time they were created. A GCC that cannot demonstrate enforced deletion of training data at the end of its permitted retention window has a compliance gap that will surface in any serious regulatory review or client data audit.
4. For GCCs serving US or European clients, have you mapped the intersection of DPDP obligations with GDPR, CCPA, or HIPAA requirements — and resolved the conflicts?
Multi-jurisdictional data governance is one of the most technically complex compliance challenges in GCC operations. GDPR and CCPA both impose requirements on how data about their respective subjects can be processed, regardless of where the processing occurs. DPDP imposes obligations on data about Indian data principals. When your GCC’s AI systems process data that is subject to multiple frameworks simultaneously — a common situation in BFSI and healthcare GCCs — you need a documented compliance architecture that satisfies all applicable requirements, not just the most prominent one.
Compliance Architecture
Does your GCC hold ISO 27001 certification — and does that certification explicitly cover your AI systems and data environments?
ISO 27001 certification signals to clients, investors, and regulators that your organisation has implemented a systematic information security management framework. It is now the minimum expected standard for GCCs handling cross-border data flows. However, many GCCs hold certifications that were scoped before their AI systems were deployed — meaning the certification does not cover the most sensitive parts of the technology environment. Enorbe maintains ISO 27001 certification as a baseline standard and recommends that GCC clients pursue certification scoped to include AI systems as part of the first-year operating roadmap.
8. If you use AI in any part of your recruitment or HR processes, have you documented compliance with the EU AI Act’s high-risk AI classification for recruitment tools?
From August 2026, the EU AI Act classifies recruitment AI as high-risk, with compliance requirements including documented risk management processes, data quality controls, bias testing, and meaningful human oversight. Penalties reach EUR 35 million or 7% of global annual turnover for non-compliance. For GCCs serving European clients or using AI tools that process data about European job applicants, this is not a future concern — it is current regulatory exposure. Audit whether your AI screening tools have documented compliance, whether your hiring team can describe the human oversight process, and whether you have bias-testing evidence on file.
Organisational Readiness
- Does your GCC have a named individual accountable for AI governance — and does that person have the authority, budget, and reporting line to act on AI risk?
The NASSCOM-Zinnov GCC Landscape Report July 2026 found that 64% of GCC site leaders now hold dual mandates combining global functional ownership with AI governance responsibilities. This reflects a broader industry shift: AI governance is no longer a function that can be assigned to a compliance team as a side responsibility. It requires dedicated ownership, budget for ongoing monitoring and incident response, and a reporting line that reaches the board — either directly or through the GCC’s global sponsor. If your GCC cannot name the person accountable for AI governance today, that is the first gap to close.
10. When did you last run a structured AI risk assessment — and does it cover the specific AI use cases your GCC is deploying, not just general AI risk categories?
Generic AI risk frameworks are a starting point, not an endpoint. The risk profile of an LLM-powered customer support application is materially different from that of an AI-assisted financial modelling tool or an automated code review system. Enorbe’s GCC advisory includes use-case-specific AI risk assessment as a standard component of GCC governance design — mapping risk to the actual AI applications deployed, not to a theoretical model of AI in general.
What to Do With Your Audit Results
If you worked through all 10 checkpoints and found no gaps, either your GCC is genuinely among the most mature AI governance operations in India’s ecosystem — or the audit was not applied honestly. Most GCCs in 2026 will find gaps in two to four areas. The priority sequence for remediation is:
- Data governance first: DPDP compliance gaps create the most immediate regulatory exposure and the most reputational risk with enterprise clients. Map your data flows, document your consent architecture, and establish enforced retention policies before anything else.
- Certification coverage second: If your ISO 27001 scope does not cover your AI systems, extend it. If you do not hold certification, begin the process. This is the credential that enterprise clients and investors use as a proxy for governance maturity.
- Accountability structure third: Name the person responsible for AI governance and give them the authority to act. This single structural change reduces AI risk faster than any technology investment.
- Infrastructure and compliance architecture fourth: These are longer-horizon workstreams that require technical design and legal input. They are important — but they follow from, and depend on, the governance structure that the first three steps establish. Enorbe’s GCC advisory team works with mid-market companies to sequence these workstreams correctly, avoiding the common failure mode of investing in technology architecture before the governance layer that gives it meaning.
If this audit surfaced gaps you are not sure how to close, Enorbe’s advisory team can help. We work with US and UK mid-market companies to design AI-ready GCC architectures that satisfy board scrutiny, regulatory requirements, and enterprise client due diligence — from day one. Book a consultation at enorbe.com/contact-us or write to us at info@enorbe.com.
