AI Security & Compliance India
Secure your AI deployments against the emerging threat landscape — prompt injection, data leakage, model theft, and adversarial manipulation. Opsio delivers AI security engineering and DPDPA compliance for Indian enterprises deploying LLMs, ML models, and automated decision systems.
Trusted by 100+ organisations across 6 countries
OWASP
LLM Top 10
Red Team
AI Testing
DPDPA
AI Compliance
24/7
Monitoring
What's Included
LLM Red Teaming & Penetration Testing
Systematic adversarial testing of your LLM deployments — prompt injection, jailbreaking, data extraction, and role manipulation attacks conducted by certified AI security engineers. Comprehensive vulnerability report with risk ratings and prioritised remediation guidance.
Guardrail Implementation & Hardening
Production guardrail systems using Guardrails AI, NeMo Guardrails, and custom validation layers. Input filtering for prompt injection, output filtering for PII leakage including Aadhaar and PAN masking, topic enforcement, and response quality controls — deployed with minimal latency impact.
DPDPA AI Compliance Engineering
Technical controls for DPDPA automated decision-making provisions — consent management for AI processing, data minimisation in training pipelines, right to explanation implementation, human review mechanisms, and data subject access request handling for AI-generated decisions.
Model Security & Access Controls
Model registry security, API authentication and rate limiting, model encryption at rest and in transit, intellectual property protection against model extraction attacks, and access controls ensuring only authorised systems and users can invoke model endpoints.
AI Threat Monitoring & Detection
Real-time monitoring of model inputs and outputs for adversarial patterns, prompt injection attempts, data exfiltration indicators, and anomalous usage. Integration with SIEM platforms and CERT-In incident reporting workflows for comprehensive AI threat visibility.
Training Data Security & Privacy
Data lineage tracking, PII detection and removal from training datasets, differential privacy implementation for sensitive Indian data, secure data room environments for model training, and access controls ensuring training data governance aligns with DPDPA requirements.
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Our AWS migration has been a journey that started many years ago, resulting in the consolidation of all our products and services in the cloud. Opsio, our AWS Migration Partner, has been instrumental in helping us assess, mobilize, and migrate to the platform, and we're incredibly grateful for their support at every step.

Roxana Diaconescu
CTO · SilverRail Technologies
Securing India's AI Deployments at Scale
Indian enterprises are deploying large language models, computer vision systems, and ML-driven decision engines at unprecedented pace — but AI security has not kept up. LLM-powered chatbots can be manipulated through prompt injection to reveal confidential data, bypass content filters, or execute unintended actions. ML models can be poisoned through training data manipulation, stolen through model extraction attacks, or fooled by adversarial inputs crafted to cause misclassification. These are not theoretical risks — they are active exploits being used against production AI systems globally. DPDPA introduces specific obligations for organisations processing personal data through automated systems, and CERT-In's incident reporting requirements now explicitly cover AI-related security incidents. RBI's evolving guidance on AI in financial services demands that banks and NBFCs demonstrate security controls around AI systems making credit decisions, fraud detection, and customer interaction automation. Indian enterprises deploying AI without dedicated security engineering face regulatory penalties, data breach liability, and operational risk from compromised AI systems.
Opsio's AI security practice addresses the complete OWASP Top 10 for LLMs — prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. We implement defence-in-depth strategies combining input validation, output filtering, guardrail systems, monitoring, and access controls to protect your AI deployments within Indian regulatory and threat landscapes.
Our red team engagements simulate real-world attack scenarios against your AI systems — attempting prompt injection, jailbreaking, data extraction, adversarial input crafting, and model manipulation to identify vulnerabilities before malicious actors do. Red team findings are translated into actionable remediation plans with prioritised fixes, architectural recommendations, and guardrail configurations that close identified gaps systematically.
For LLM deployments, we implement production guardrail systems using tools like Guardrails AI, NeMo Guardrails, and custom validation layers that filter prompts and responses in real time. Input validation catches prompt injection attempts, output filtering prevents sensitive data leakage including Aadhaar numbers, PAN details, and financial information, and topic controls keep the LLM within its intended domain. These guardrails operate with minimal latency impact, preserving user experience whilst enforcing security boundaries.
AI security is not a one-time assessment — it is an ongoing discipline requiring continuous monitoring, threat intelligence, and adaptation as attack techniques evolve. Opsio provides managed AI security operations including real-time prompt monitoring, anomaly detection across model inputs and outputs, automated guardrail updates based on emerging threats, and incident response for AI-specific security events aligned with CERT-In reporting timelines. Our IST-based security operations centre ensures rapid response to AI threats targeting your Indian deployments. Featured reading from our knowledge base: NCIIPC Guidelines and OT Security in India: A Complete Compliance Guide, Top 10 AWS Cloud Security Tools for Identity and Compliance – Opsio, and Cloud Compliance: Ensuring Security and Regulatory Compliance – Opsio. Related Opsio services: IoT Predictive Maintenance for India, IoT Solution Development India, AI Chatbot Development for India, and AI Governance & Ethics Consulting India.
How Opsio Compares
| Capability | No AI Security | Generic Security Team | Opsio AI Security |
|---|---|---|---|
| Prompt injection defence | Unprotected | Basic input validation | Multi-layer guardrails + monitoring |
| Red teaming | None | Traditional pen test only | AI-specific adversarial testing |
| PII protection (Aadhaar/PAN) | Uncontrolled | Manual review | Automated detection + masking |
| DPDPA AI compliance | None | Generic data protection | AI-specific DPDPA controls |
| Threat monitoring | None | Generic SIEM alerts | AI-specific threat detection |
| Incident response | Ad-hoc | Generic IR process | AI-specific playbooks + CERT-In aligned |
| Typical implementation | N/A | Partial coverage | 8-12 weeks full coverage |
Ready to get started?
What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
AI Security Assessment
₹10,00,000–₹25,00,000
One-time
Red Team + Guardrails
₹20,00,000–₹45,00,000
Per engagement
Managed AI Security
₹4,00,000–₹10,00,000/mo
Ongoing
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
OWASP LLM expertise
Deep knowledge of the OWASP Top 10 for LLMs with proven remediation across production deployments.
Indian compliance focus
DPDPA, CERT-In, RBI, and IRDAI requirements woven into every AI security engagement.
Red team capability
Dedicated AI red team simulating prompt injection, jailbreaking, and adversarial attacks against your systems.
Production guardrails
Battle-tested guardrail deployments protecting LLMs in BFSI, e-commerce, and government applications.
24/7 AI monitoring
Continuous threat detection across model inputs and outputs with IST-based incident response.
Defence-in-depth approach
Layered security — input validation, output filtering, access controls, monitoring, and incident response combined.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
AI Security Assessment
Inventory AI systems, map attack surfaces, evaluate existing controls, and assess DPDPA and CERT-In compliance posture. Deliverable: AI security risk report. Timeline: 2-3 weeks.
Red Team & Penetration Testing
Adversarial testing of LLM deployments, ML models, and AI APIs for prompt injection, data leakage, and manipulation vulnerabilities. Timeline: 2-4 weeks.
Guardrail Implementation
Deploy production guardrails, configure input/output filtering, implement DPDPA compliance controls, and harden model access and API security. Timeline: 3-5 weeks.
Managed AI Security Operations
Continuous monitoring, threat detection, guardrail updates, incident response, and quarterly reassessment as your AI deployments evolve. Timeline: Ongoing.
Key Takeaways
- LLM Red Teaming & Penetration Testing
- Guardrail Implementation & Hardening
- DPDPA AI Compliance Engineering
- Model Security & Access Controls
- AI Threat Monitoring & Detection
Industries Served by Opsio
BFSI
LLM security for banking chatbots and ML model protection for credit scoring systems.
Healthcare
AI security for clinical decision support and medical imaging systems.
E-commerce
Guardrails for product recommendation AI and conversational commerce bots.
Government & Defence
AI security for citizen-facing AI systems and sensitive government deployments.
AI Security & Compliance India — FAQ
What is prompt injection and why is it dangerous?
Prompt injection is an attack where malicious users craft inputs that override the LLM's system instructions — causing it to reveal confidential data, bypass content filters, generate harmful content, or execute unintended actions. For Indian BFSI chatbots handling financial data or government bots processing citizen information, a successful prompt injection could expose Aadhaar numbers, account details, or internal policies. It is the most critical vulnerability in LLM deployments today.
What is the OWASP Top 10 for LLMs?
The OWASP Top 10 for Large Language Models identifies the ten most critical security risks: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. Opsio's AI security assessments evaluate your LLM deployments against all ten categories and implement controls for each identified risk.
How does DPDPA affect AI security requirements in India?
DPDPA requires organisations to implement reasonable security safeguards for personal data processed by AI systems, provide transparency about automated decision-making, enable human review of significant automated decisions, and report data breaches involving AI systems within CERT-In mandated timelines. For AI systems processing Aadhaar, PAN, financial, or health data of Indian citizens, DPDPA compliance requires documented security controls around the entire AI pipeline.
What is the typical investment for AI security services in India?
An AI security assessment runs ₹10,00,000 to ₹25,00,000 depending on the number of AI systems in scope. Red team engagements cost ₹15,00,000 to ₹35,00,000 for comprehensive adversarial testing. Guardrail implementation ranges from ₹20,00,000 to ₹45,00,000 per deployment. Managed AI security operations cost ₹4,00,000 to ₹10,00,000 per month. Most organisations start with assessment and red team to identify priorities before committing to full implementation.
How long does an AI security engagement take?
A complete AI security engagement — from assessment through guardrail deployment — typically takes 8-12 weeks. The security assessment runs two to three weeks, red team testing takes two to four weeks (can run in parallel with assessment), guardrail implementation takes three to five weeks, and validation and handover adds one to two weeks. Managed operations begin immediately after deployment.
Can you secure LLMs that are already in production?
Yes. Most of our engagements involve securing existing LLM deployments. We implement guardrails and monitoring without requiring application redesign — adding input validation layers, output filtering, and monitoring as middleware components that integrate with your existing architecture. For critical vulnerabilities discovered during red teaming, we provide immediate mitigation while longer-term architectural improvements are planned. Indian enterprises running LLMs on AWS SageMaker Mumbai or Azure OpenAI Service benefit from region-specific guardrail tuning that addresses Hindi and regional language prompt injection vectors. All security implementations maintain DPDPA compliance for personal data flowing through LLM pipelines.
How do guardrails affect LLM response latency?
Well-implemented guardrails add 50-150ms to response latency — imperceptible to users in conversational interfaces. Our guardrail architecture uses parallel processing for input validation, streaming-compatible output filtering, and optimised rule engines that minimise latency impact. For latency-sensitive applications, we offer tiered guardrail configurations that balance security stringency with performance requirements. Deploying guardrails on AWS ap-south-1 Mumbai or Azure Central India ensures minimal network latency for Indian users, while edge caching of frequently triggered rules further reduces overhead. Indian BFSI applications requiring sub-second response times can use our lightweight guardrail tier optimised for INR-conscious infrastructure budgets.
What is AI red teaming and how does it differ from traditional penetration testing?
AI red teaming specifically targets AI and ML systems with adversarial techniques — prompt injection crafting, jailbreak attempts, training data extraction, model manipulation, and adversarial input generation. Traditional penetration testing focuses on network, application, and infrastructure vulnerabilities. Both are necessary for AI-powered applications, but AI red teaming requires specialised knowledge of machine learning vulnerabilities and attack methodologies that traditional security testers typically lack.
How do you handle Aadhaar and PAN data in AI systems?
We implement multi-layered PII protection: training data scanning and removal of Aadhaar and PAN numbers before model training, input validation detecting and masking PII before it reaches the LLM, output filtering catching any PII that appears in model responses, and audit logging for compliance. For BFSI applications, additional controls ensure financial data processed by AI systems meets RBI data protection standards.
Can AI security be integrated into our existing SOC?
Yes. Our AI threat monitoring integrates with existing SIEM platforms including Splunk, Sentinel, and QRadar via standard log forwarding and API integration. AI-specific alerts — prompt injection attempts, anomalous model queries, data exfiltration indicators — flow into your existing SOC workflow alongside traditional security events. We provide detection rules, runbooks, and training for your SOC analysts on AI-specific threat triage.
Still have questions? Our team is ready to help.
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