AI Security is the practice of protecting artificial-intelligence systems — the models, the data that trains them, the pipelines that build them, and the applications that use them — from a new class of threats that traditional cybersecurity tools were never designed to catch. As Indian enterprises race to deploy LLMs, chatbots, copilots and agentic AI, they're opening attack surfaces that most security teams have never tested. Invitty provides specialist AI security services in Chennai and across India: LLM penetration testing, AI red teaming, model and data protection, and AI governance that lets you adopt AI safely instead of hoping for the best.
Generative AI changes the risk equation in both directions — attackers use it to find and exploit vulnerabilities faster, and your own AI systems become targets. Prompt injection, data poisoning, model theft, jailbreaks and sensitive-data leakage are already causing real incidents. Our AI security practice is built around the OWASP Top 10 for LLM Applications and emerging standards like the NIST AI Risk Management Framework and ISO/IEC 42001.
Our AI security services
LLM Application Penetration Testing
We test your AI-powered applications against the OWASP Top 10 for LLMs — prompt injection (direct and indirect), insecure output handling, training-data poisoning, model denial-of-service, supply-chain risks, sensitive-information disclosure, insecure plugin design, excessive agency, overreliance and model theft. If your chatbot can be tricked into leaking data, ignoring guardrails or executing unintended actions, we'll find it before your users — or attackers — do.
AI Red Teaming
Goal-based adversarial testing of your AI system: we attempt jailbreaks, guardrail bypasses, harmful-content generation, and extraction of system prompts, training data or proprietary model behaviour. Essential before you expose an AI feature to customers or the public.
Model & Data Protection
Securing the assets that make your AI valuable — protecting model weights from theft, defending against data-poisoning and membership-inference attacks, securing the training and fine-tuning pipeline, and preventing your proprietary or customer data from leaking through the model.
AI Governance & Risk Assessment
Practical governance so your AI adoption is defensible — an AI asset inventory, risk assessment against the NIST AI RMF, acceptable-use and data-handling policies, human-oversight controls, and an ISO/IEC 42001-aligned AI management system. This is what boards, auditors and enterprise customers increasingly ask for.
Securing AI Adoption (Shadow AI)
Your employees are already using ChatGPT, Copilot and other tools — often pasting sensitive data into them. We help you discover shadow-AI usage, set guardrails (which tools, which data, which direction), and deploy DLP and controls so you get AI's productivity without leaking source code, PII or trade secrets.
Why AI security is different from traditional cybersecurity
Classic security assumes deterministic systems — the same input gives the same output, and you patch known vulnerabilities. AI is probabilistic and manipulable through language itself: an attacker doesn't need to exploit a code bug when they can simply persuade your model with a crafted prompt. Models can leak the data they were trained on, be poisoned during training, or be coaxed into actions their designers never intended. Traditional firewalls, scanners and WAFs don't see any of this — which is why AI systems need purpose-built testing and controls.
Who needs AI security
Any company building or deploying AI features — SaaS products adding copilots, enterprises rolling out internal chatbots, fintechs using AI for decisions, healthcare using AI on patient data. Regulated businesses where an AI mistake or breach carries legal and DPDPA consequences. And any organization whose employees use generative AI — which is now essentially everyone. If AI touches your data, customers or decisions, it needs to be secured and governed.
AI security and compliance
AI security is fast becoming a compliance requirement, not just best practice. DPDPA holds you responsible for personal data your AI systems process — a model leaking PII is a breach. ISO/IEC 42001 (the new AI management-system standard) and the NIST AI RMF are becoming the reference frameworks enterprise customers cite in security questionnaires. Our AI security work integrates with your DPDPA, SOC 2 and ISO 27001 programmes, and pairs naturally with Chainguard's secure AI-software supply chain.
Why choose Invitty for AI security in India
We're one of the few Indian security firms with a dedicated AI security practice rather than a bolt-on. Our testers combine deep application-security experience (see our VAPT services) with hands-on LLM and agentic-AI expertise, working to the OWASP LLM Top 10 and NIST AI RMF. We deliver developer-friendly, actionable reports — specific prompts, payloads and fixes, not vague warnings — and support you through remediation with a free re-test. Based in Chennai with delivery across India, we help you ship AI features your security team, board and customers can trust.
Frequently Asked Questions
What is AI security and how is it different from cybersecurity?
AI security protects AI systems — models, training data, pipelines and AI-powered applications — from threats that traditional security tools miss. Unlike classic systems, AI is probabilistic and can be manipulated through language itself (e.g., prompt injection), can leak its training data, or be poisoned. Firewalls and scanners don't detect these attacks, so AI needs purpose-built testing and governance.
What is LLM penetration testing?
It's security testing of applications built on large language models, against the OWASP Top 10 for LLMs — prompt injection, insecure output handling, data leakage, jailbreaks, excessive agency, model theft and more. We attempt to make your AI leak data, bypass its guardrails or take unintended actions, then give you specific fixes.
What is prompt injection and why is it dangerous?
Prompt injection is when an attacker crafts input that overrides your AI's instructions — for example, hiding commands in a document your AI summarizes, causing it to ignore safety rules, reveal its system prompt, or leak data. It's the top LLM risk because it needs no code exploit, just cleverly worded text, and most AI apps are vulnerable by default.
Do we need AI security if we just use ChatGPT or Copilot?
Yes — even without building AI, your employees using public AI tools can leak sensitive data (source code, PII, trade secrets) by pasting it into prompts. We help you discover this 'shadow AI', set guardrails on which tools and data are allowed, and deploy controls so you get AI's benefits without the data-leak risk.
What frameworks do you use for AI security?
We work to the OWASP Top 10 for LLM Applications, the NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001 (the AI management-system standard). These are the references enterprise customers and auditors increasingly cite, and they map cleanly to DPDPA, SOC 2 and ISO 27001 obligations.
How does AI security relate to DPDPA compliance?
Under DPDPA you're responsible for personal data your AI systems process — if a model leaks or mishandles PII, that's a data breach with penalties up to ₹250 crore. AI security controls (preventing data leakage, securing pipelines, governance) are part of demonstrating the 'reasonable security safeguards' DPDPA requires. We integrate the two.
Can you test our AI chatbot or copilot before we launch it?
Yes — that's exactly what our LLM penetration testing and AI red teaming do. We test for prompt injection, jailbreaks, data leakage and unsafe actions before you expose the feature to customers or the public, then re-test after you fix the findings. Testing before launch is far cheaper than a public incident.
What is AI governance and do we need it?
AI governance is the policy and oversight layer — an inventory of your AI systems, risk assessment, acceptable-use and data-handling rules, human-oversight controls, and an ISO 42001-aligned management approach. Boards, regulators and enterprise customers increasingly require it. We build practical governance that enables AI adoption rather than blocking it.
What are the biggest AI security risks for businesses?
The most common are prompt injection, sensitive-data leakage through models, training-data poisoning, jailbreaks that bypass safety controls, excessive agency (AI taking unintended actions), model theft, and shadow-AI data leaks by employees. The OWASP LLM Top 10 catalogs these — we test against all of them.
Do you provide AI security services outside Chennai?
Yes — AI security testing and governance are delivered remotely and securely across India, with on-site engagement available in Tamil Nadu and South India. We serve SaaS, fintech, healthcare and enterprise teams building or adopting AI anywhere in the country.