[vc_row css_class=”services-hero” css=”.vc_custom_1234567950{padding-top: 120px !important;padding-bottom: 80px !important;background: linear-gradient(180deg, rgba(124, 58, 237, 0.08) 0%, transparent 100%) !important;}”][vc_column width=”2/3″ offset=”vc_col-lg-offset-2 vc_col-md-offset-1″][vc_custom_heading text=”Your AI systems are already under attack. You just don’t know it yet.” font_container=”tag:h1|font_size:52px|color:%23ffffff|line_height:1.15″ google_fonts=”font_family:Inter%3A400%2C700%2C800|font_style:800%20bold%20regular%3A800%3Anormal” css_class=”hero-heading”][vc_column_text css=”.vc_custom_1234567951{margin-top: 30px !important;margin-bottom: 40px !important;}”]

I help enterprises secure their LLM integrations, RAG systems, and AI agents before vulnerabilities become breaches.

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❌ Prompt Injection Attacks

Attackers manipulate your LLM prompts to bypass restrictions, leak data, or execute unauthorized actions. Traditional WAFs don’t catch these.

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❌ Data Leakage

Your RAG system might be exposing customer data, internal documents, or PII through vector database poisoning or context injection.

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❌ Agent Compromise

Autonomous agents with tool access can be manipulated to execute unintended actions—from API abuse to financial transactions.

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Traditional security teams aren’t equipped for this.

They understand SQL injection and XSS. They know how to secure APIs and databases. But LLM security? RAG pipeline hardening? Agent sandboxing? That’s a different game.

That’s where I come in.

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Comprehensive security evaluation of your AI systems—from ChatGPT integrations to custom LLM deployments.

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What I Evaluate:

  • ✓ LLM Integration Security – API key exposure, prompt injection vulnerabilities, output validation gaps
  • ✓ RAG System Hardening – Vector database security, context poisoning, data leakage through embeddings
  • ✓ Agent Architecture Review – Tool access controls, function calling security, autonomous action boundaries
  • ✓ Data Flow Analysis – PII handling, customer data exposure, training data contamination
  • ✓ Supply Chain Audit – Third-party model risks, plugin vulnerabilities, API dependencies

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Deliverables:

Executive Summary
High-level findings and business impact for leadership.

Technical Report
Detailed vulnerability analysis with code-level examples and proof-of-concept exploits.

Remediation Roadmap
Prioritized action plan with implementation guidance for your engineering team.

Security Architecture Recommendations
Long-term hardening strategies and best practices.

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Timeline:

2-4 weeks depending on system complexity

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What’s Included:

Threat Model Workshop
Collaborative session with your team to map AI-specific attack vectors.

Red Team Testing
Active exploitation attempts against your LLM endpoints and agent systems.

Code Review
Security analysis of your prompt engineering, RAG implementation, and agent orchestration logic.

Compliance Mapping
Gap analysis against emerging AI regulations (EU AI Act, etc.).

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Ideal For:

Companies deploying customer-facing AI features, financial services integrating LLMs, healthcare apps using RAG for patient data

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Deep-dive security analysis specifically for language model integrations and RAG pipelines.

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Focus Areas:

  • ✓ Prompt Injection Defense – System prompts, user input sanitization, jailbreak prevention
  • ✓ Context Window Security – Sensitive data in prompts, conversation history leakage
  • ✓ RAG Pipeline Hardening – Document ingestion security, embedding attacks, retrieval manipulation
  • ✓ Output Validation – Hallucination detection, toxic content filtering, PII scrubbing
  • ✓ Model Security – Fine-tuning data poisoning, model extraction risks, adversarial inputs

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Timeline:

1-3 weeks depending on implementation complexity

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Tailored security consulting for your specific AI security needs.

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Available Services:

  • ✓ Incident Response – AI-specific breach investigation and containment
  • ✓ Security Architecture Design – Build secure-by-design AI systems from the ground up
  • ✓ Ongoing Advisory – Monthly retainer for continuous security guidance
  • ✓ Team Training – Embedded security coaching for your AI/ML engineers
  • ✓ Compliance Consulting – EU AI Act, NIST AI RMF, sector-specific regulations
  • ✓ Pre-Launch Security – Security review before major AI feature releases

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How It Works:

Step 1: Discovery
We discuss your specific challenge—incident response, architecture review, or ongoing support.

Step 2: Scoping
I design a custom engagement tailored to your needs and timeline.

Step 3: Execution
Hands-on work—whether that’s incident response, security design, or team enablement.

Step 4: Knowledge Transfer
Your team leaves with both solutions and the ability to maintain security long-term.

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Engagement Models:

Project-based, monthly retainer, or hourly consulting

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1

Understand Your Stack

Deep technical discovery—architecture diagrams, code review, threat modeling workshop with your team.

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2

Attack Like an Adversary

Active exploitation—prompt injection, context manipulation, agent compromise. I find what attackers will find.

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3

Document Everything

Detailed reports with proof-of-concept exploits, severity ratings, and actionable remediation steps.

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4

Enable Your Team

Knowledge transfer sessions so your engineers can maintain security as AI systems evolve.

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Enterprise Teams

You’ve integrated ChatGPT or Claude into customer-facing features. Now you need to secure it before something goes wrong.

Financial services, healthcare, SaaS platforms

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AI-First Startups

Your product IS the AI. Security can’t be an afterthought. You need it baked in from day one.

LLM-powered apps, AI agents, RAG platforms

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Security Teams

Your developers are deploying AI. You know traditional security doesn’t cover this. You need expert guidance.

CISOs, security architects, AppSec teams

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How much does this cost?

It depends on your system complexity and engagement scope. AI Security Assessments typically start at $15K. LLM Security Reviews start at $10K. Custom engagements are quoted based on your specific needs. Book a discovery call for an exact quote.

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How long does an assessment take?

Most assessments take 2-4 weeks from kickoff to final report. Rush engagements are available for incident response situations.

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Do you work internationally?

Yes. All services are available remotely. I’m based in India but work with companies globally across US, Europe, and Asia.

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What if we’re just starting with AI?

Perfect timing. Security Architecture Design engagements help you build secure systems from day one—much cheaper than retrofitting security later.

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Do you sign NDAs?

Yes, always. Your code, architecture, and vulnerabilities remain completely confidential.

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What makes you different from traditional penetration testing firms?

Traditional pentesting firms run automated scanners and look for SQL injection. I understand how LLMs work, how RAG systems fail, how agents can be manipulated. I hold a patent in AI security. I work at GoDaddy securing AI at scale. This isn’t generic pentesting—it’s AI-specific expertise.

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Book a 30-minute discovery call. No sales pitch. Just an honest conversation about your AI security risks and how to address them.

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Or email me directly: kaushal@bhavsar.ai

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