LLM & AI red teaming
Prompt injection, jailbreak, data exfiltration and tool-abuse testing of chatbots, agents and RAG apps.
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Securing AI & AI for Security
AI features create new ways to leak data or be manipulated. We test chatbots, agents and RAG systems against prompt injection, data exfiltration and abuse, set up governance for responsible AI use, and apply AI to strengthen your own threat detection.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
Our security team tests AI systems the way attackers do, using the OWASP Top 10 for LLM Applications as a baseline, then helps you fix issues with guardrails, permissions and monitoring. For compliance, we document how AI uses personal data and who is accountable.
Discuss your requirementCompanies launching customer-facing chatbots or AI agents
Enterprises letting staff use GenAI tools with company data
Regulated firms needing AI governance for audits
Security teams wanting AI-assisted detection and triage
Capabilities
Prompt injection, jailbreak, data exfiltration and tool-abuse testing of chatbots, agents and RAG apps.
Conventional web and API security testing of the application around the model.
Input and output filtering, PII redaction, permission scoping and rate limits.
AI inventory, risk assessment, acceptable-use policy and approval workflows aligned with the DPDP Act and ISO/IEC 42001.
Machine learning and LLM assistants that detect anomalies and speed up alert triage for your SOC.
Logging of prompts and actions, abuse alerts and response plans for AI incidents.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
We list AI systems, data flows, users and connected tools.
Likely attacks and their impact are mapped for each system.
Red-team and VAPT testing against the agreed scope.
Guardrails, permissions and fixes are implemented with your team.
Policies, registers and monitoring are put in place.
Deliverables
Frameworks & tools
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
Prompt injection is when text from a user, email or web page tricks an AI system into ignoring its instructions, revealing data or taking unintended actions. It is the leading risk for LLM applications, especially agents connected to tools.
Yes, at least a light version. Staff may paste customer or confidential data into public tools. A clear policy, approved tools on business terms and basic monitoring reduce that risk significantly.
Yes, with written authorisation from you. We test the deployed application and share findings your vendor can fix, followed by a retest.
It is the international standard for AI management systems, covering governance, risk management and responsible AI practices. We help organisations align with it as part of their wider AI governance.
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ExploreOn-site meetings across Delhi NCR and Haryana from our Rohtak office; remote delivery across India.
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Receive a preliminary project architecture, pricing tiers, and timeline estimate within 24 hours under strict NDA.