AI Security Audit & LLM Security Testing Services

An AI security audit assesses the unique risks introduced by AI applications and large language models — including prompt injection attacks, sensitive data leakage through AI outputs, and weaknesses in how AI systems are governed. As more Indian businesses adopt GenAI tools and build AI-powered products, this is an increasingly urgent and, so far, under-tested category of risk — traditional application security testing wasn’t designed to catch AI-specific attack techniques. PS INFOSEC’s approach is built around the OWASP LLM Top 10 risk categories.

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What Is an AI Security Audit?

AI applications and large language models introduce a genuinely new category of security risk that traditional web and API testing wasn’t designed to catch. A prompt injection attack, for example, manipulates an AI system’s input to make it ignore its intended instructions or reveal information it shouldn’t — there’s no direct equivalent in traditional web application security, and standard testing methodologies don’t look for it.

An AI security audit reviews your AI-powered application specifically for these risks: how it handles untrusted input, whether it can leak sensitive training or context data, and whether appropriate governance and guardrails exist around its use — going beyond a standard web application test to cover the AI-specific attack surface.

What We Assess

AI Application Security Review

Reviewing the overall architecture and integration of AI features within your application for security weaknesses

LLM Security Assessment

Testing the language model integration specifically for known LLM-specific vulnerability classes

Prompt Injection Risk Assessment

Testing whether the system can be manipulated into ignoring its intended instructions through crafted input

Sensitive Data Exposure Review

Checking whether the AI system can be made to leak training data, system prompts, or other users' context data

AI Governance Assessment

Reviewing policies and guardrails around how AI is used, monitored, and controlled within your organization

AI Model Risk Assessment

Assessing risks specific to the model itself — including over-reliance on outputs without human verification in high-stakes contexts

Our AI Security Testing Approach

Why AI Systems Need a Different Testing Approach

Traditional application security testing assumes a relatively predictable set of attack techniques — SQL injection, broken authentication, and similar well-understood vulnerability classes. AI systems, particularly those built on large language models, introduce attack techniques that don’t fit that mold: an attacker doesn’t need to find a coding flaw if they can simply convince the AI system, through carefully crafted input, to behave in an unintended way.

This is a newer testing discipline than traditional web or network security, which is exactly why it’s worth a dedicated assessment rather than assuming standard web application testing already covers it.

What You Get: Deliverables

Prompt Injection Findings

Documented instances where the system's intended behavior could be overridden

Data Exposure Report

Any sensitive data the system could be manipulated into revealing

Governance Gap Analysis

Where AI usage policies and guardrails need strengthening

Remediation Guidance

AI-specific mitigation recommendations, not generic application security advice

Industries We Serve

We’ve delivered engagements across a range of sectors, including:

Banking & Financial
Healthcare & Life Sciences
Information Technology & SaaS
Manufacturing
Government & Public Sector
Retail & E-Commerce
Education
Telecommunications
Logistics & Transportation
Professional Services

Frequently Asked Questions

What is prompt injection?

A prompt injection attack manipulates an AI system's input to make it ignore its intended instructions or reveal information it shouldn't — one of the most common and serious risks in LLM-powered applications, and a technique with no direct equivalent in traditional web application security.

Is this different from regular application security testing?

Yes — traditional web and API testing doesn't cover AI-specific risks like prompt injection or model behavior manipulation. An AI security audit is a distinct, additional layer of testing specifically for AI-powered features, typically run alongside standard web/API testing rather than replacing it.

What is the OWASP LLM Top 10?

A framework, analogous to the well-known OWASP Top 10 for web applications, that catalogs the most critical security risks specific to large language model applications — including prompt injection, insecure output handling, and training data poisoning. Our testing approach is built around these categories.

Do you test both the AI model and the application built around it?

Yes — the assessment covers both the AI/LLM component's specific behavior and how it's integrated into the broader application, since risks can arise from either layer.

Who needs an AI security audit?

Any business building AI-powered products or features, or using LLMs — internally or customer-facing — that process sensitive business or customer data, especially before a public launch or when adding new AI capabilities to an existing product.