L&T's Vyoma.AI: What Indian SMBs Need to Know About AI Data Centre Security

What Happened

Indian infrastructure giant Larsen & Toubro (L&T) has incorporated Vyoma.AI Ltd, a wholly owned subsidiary designed to establish India's first domestically-built AI data centre infrastructure. This is a significant move in India's push toward data sovereignty and reducing dependence on foreign cloud providers for AI workloads.

Vyoma.AI will focus on building specialised data centres optimised for artificial intelligence and machine learning workloads, with a particular emphasis on serving Indian enterprises that need to keep sensitive data within national borders. The subsidiary comes at a time when Indian businesses are increasingly concerned about data residency requirements under the Digital Personal Data Protection (DPDP) Act, 2023 and regulatory pressure from CERT-In and the RBI.

This isn't just infrastructure news—it's a watershed moment for Indian SMBs navigating the complex intersection of AI adoption, regulatory compliance, and cybersecurity. When I was architecting security for large enterprises, we constantly battled the tension between innovation velocity and data governance. Vyoma.AI addresses exactly that friction point for India.

Why This Matters for Indian Businesses

Let me be direct: this changes everything for Indian SMBs considering cloud migration.

For years, Indian businesses have faced an uncomfortable choice:

1. Use global cloud providers (AWS, Azure, GCP) and navigate complex data residency compliance
2. Build expensive, in-house infrastructure
3. Risk regulatory penalties under DPDP Act and RBI guidelines

The Compliance Advantage

Under the DPDP Act 2023, sensitive personal data must be stored in India. The RBI's guidelines (issued in 2021, reinforced in 2023) require banks and fintech companies to keep customer data within national borders. CERT-In's 6-hour breach notification mandate means you need infrastructure you can audit and control immediately.

With Vyoma.AI:

1. Data never leaves Indian territory
2. You have a domestic vendor accountable under Indian law
3. Compliance audits become simpler (no cross-border data flow complexity)
4. You reduce latency for Indian users

The Security Angle

As someone who's reviewed hundreds of Indian SMB security postures, I can tell you: most don't fully understand their cloud provider's security responsibilities. AWS, Azure, and GCP operate under the "Shared Responsibility Model"—they secure the infrastructure, you secure your data and access controls.

With a domestic provider like Vyoma.AI:

1. You can request on-site security audits
2. CERT-In can conduct investigations without cross-border legal friction
3. You have direct accountability to Indian regulators
4. Incident response timelines compress dramatically

Technical Breakdown: How AI Data Centre Security Works

Let me walk you through the architecture and threat model of AI-optimized data centres like what Vyoma.AI will operate.

graph TD
    A[Data Ingestion Layer] -->|TLS 1.3| B[Network Security]
    B -->|Firewall/WAF| C[Kubernetes Cluster]
    C -->|RBAC + Pod Security| D[AI Workload Execution]
    D -->|Encryption at Rest| E[Storage Layer]
    E -->|Audit Logging| F[Compliance & Monitoring]
    F -->|SIEM Integration| G[Incident Response]
    H[Threat: Data Exfiltration] -.->|Attempts to bypass| B
    I[Threat: Privilege Escalation] -.->|Targets| C
    J[Threat: Model Poisoning] -.->|Attacks| D

Key Security Layers in AI Data Centres

1. Network Segmentation AI workloads handle massive data flows. Vyoma.AI will need microsegmentation—isolating each AI model's network traffic from others. This prevents lateral movement if one tenant's workload is compromised.

# Example: Kubernetes Network Policy (what Vyoma.AI will likely use)
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: ai-workload-isolation
spec:
  podSelector:
    matchLabels:
      app: ml-model-prod
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - podSelector:
        matchLabels:
          app: api-gateway
    ports:
    - protocol: TCP
      port: 8080
  egress:
  - to:
    - podSelector:
        matchLabels:
          app: data-store
    ports:
    - protocol: TCP
      port: 5432

2. Encryption in Transit & at Rest AI models are valuable IP. Data flowing through the centre must be encrypted with TLS 1.3 (minimum). Data at rest must use AES-256 encryption with keys managed by an HSM (Hardware Security Module).

# Verify TLS 1.3 is enforced on your cloud connection
openssl s_client -connect your-ai-provider.in:443 -tls1_3

# Output should show: "TLSv1.3" in the protocol line
# If you see TLS 1.2 or lower, escalate to your provider immediately

3. GPU/TPU Access Control AI workloads run on specialized hardware (NVIDIA GPUs, TPUs). These must be isolated per tenant with strict RBAC (Role-Based Access Control). A compromised user account shouldn't access another tenant's GPU memory.

4. Model & Data Poisoning Prevention This is the new frontier. Attackers can inject malicious data into training sets, corrupting AI models. Vyoma.AI will need:

1. Data validation pipelines
2. Model integrity verification
3. Anomaly detection on training inputs
4. Audit trails for all model updates

5. Compliance Logging & Audit Under DPDP Act, you must log:

1. Who accessed what data, when, and why
2. All encryption key rotations
3. All user privilege changes
4. All data export requests

# Example: Query audit logs for unauthorized access attempts
# (This is what Vyoma.AI's security team will monitor 24/7)
grep "UNAUTHORIZED_ACCESS" /var/log/audit/compliance.log | wc -l

# For CERT-In compliance, these logs must be retained for 180 days minimum
find /var/log/audit -name "*.log" -mtime +180 -delete

How to Protect Your Business When Choosing an AI Data Centre

If you're considering migrating AI workloads to Vyoma.AI or any domestic provider, here's your security checklist:

Quick Fix: Audit Your Current Cloud Setup

Before moving to Vyoma.AI, understand your current risk:

# Step 1: List all cloud storage buckets and their encryption status
aws s3api list-buckets --query 'Buckets[].Name' --output text | \
while read bucket; do
  encryption=$(aws s3api get-bucket-encryption --bucket $bucket 2>/dev/null)
  if [ -z "$encryption" ]; then
    echo "⚠️  RISK: $bucket has NO encryption"
  else
    echo "✓ $bucket is encrypted"
  fi
done

# Step 2: Check if data leaves India
aws s3api get-bucket-location --bucket your-bucket --query 'LocationConstraint'
# Should return: ap-south-1 (Mumbai) or ap-south-2 (Hyderabad)
# If it returns us-east-1 or eu-west-1, your data is outside India

# Step 3: Verify access logging is enabled
aws s3api get-bucket-logging --bucket your-bucket
# Should show LoggingEnabled with a target bucket

How Bachao.AI Detects These Risks

This is exactly why I built Bachao.AI—to make enterprise-grade security accessible to Indian SMBs making these infrastructure decisions.

What's Next for Indian SMBs?

Vyoma.AI's launch signals a major shift: India is building its own AI infrastructure stack. This is good news for compliance, latency, and long-term data sovereignty.

But it also means:

1. Your security responsibilities don't disappear—they shift
2. You still need to audit your data flows, encryption, and access controls
3. CERT-In and DPDP compliance requirements remain unchanged
4. Incident response timelines are now faster (which is good, but requires preparation)

If you're considering Vyoma.AI, now is the time to:

1. Audit your current security posture
2. Understand your DPDP compliance gaps
3. Plan your data migration strategy
4. Set up monitoring and incident response before you move

Originally reported by Inc42

Written by Shouvik Mukherjee, Founder of Bachao.AI. Follow me on LinkedIn for daily cybersecurity insights for Indian businesses.

Written by Shouvik Mukherjee, Founder of Bachao.AI. Follow me on LinkedIn for daily cybersecurity insights for Indian businesses.