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Secure Your AI Before It Becomes
Your Biggest Risk

Generative AI is transforming business, but it also introduces new security risks. Protect your AI applications against prompt injection, data leakage, model abuse, and emerging OWASP LLM threats. 

AI Security Assessments

OWASP LLM Risk Validation

Secure Enterprise AI Adoption

01

Scan

See the threat instantly

02

Understand

Know how you're protected

03

Trust

Proven results & coverage

04

Act

Book your assessment

BUILD AI SECURITY INTO EVERY LLM APPLICATION

Large Language Models are rapidly being integrated into customer portals, internal assistants, copilots, enterprise search, automation platforms, and AI agents. While these applications improve productivity, they also introduce an entirely new attack surface that traditional application security tools were never designed to protect.

Mechsoft helps organizations securely adopt Generative AI by identifying vulnerabilities unique to LLM applications and implementing security controls aligned with the OWASP Top 10 for LLM Applications. Our cybersecurity specialists evaluate AI architecture, prompts, APIs, plugins, retrieval pipelines, and business workflows to identify exploitable risks before attackers do. We help organizations build AI applications that remain secure, compliant, and resilient as AI adoption grows.

Our solution ensures every message — whether inbound or outbound — is scanned, analyzed, and secured before it can harm your organization.

THE AI SECURITY CHALLENGES ORGANIZATIONS FACE TODAY

Prompt Injection

Attackers manipulate prompts to bypass safeguards or influence AI behaviour.

Sensitive Data Exposure

AI applications may unintentionally expose confidential business information or customer data.

Untrusted AI Integrations

Plugins, external tools, APIs, and third-party models introduce additional supply chain risks.

AI Hallucinations

Incorrect or fabricated responses can influence business decisions and reduce trust.

Excessive AI Permissions

AI assistants may receive more access than necessary, increasing business risk.

Rapid AI Adoption

Organizations often deploy AI faster than security governance can keep pace.

HOW MANY OF THESE SOUND FAMILIAR?

Employees are using public AI tools without governance.

AI applications have access to sensitive business data.

Prompt security has never been tested.

AI responses are trusted without verification.

AI plugins and integrations have not undergone security assessment.

There is no formal AI security policy.

AI applications are deployed without dedicated security testing.

Reality Check :-If several of these challenges sound familiar, your organization should assess AI security before expanding enterprise AI adoption. 

THE PROCESS
INTERACTIVE

HOW OWASP LLM SECURITY ASSESSMENT WORKS

Select a step to see what happens behind the scenes.

Discover

Assess

Review AI architecture, business use cases, and model integrations. Review AI architecture, business use cases, and model integrations. 

AI architecture review Business use case analysis Model integration assessment
Classify

Discover

Identify AI assets, models, APIs, plugins, vector databases, and retrieval systems. 

AI asset discovery Model and API inventory Vector database identification

Assess  

Test

Evaluate AI applications against OWASP LLM risks using controlled attack scenarios. 

OWASP LLM risk testing Prompt attack simulation AI security validation

Monitor

Validate

Identify exploitable prompt injection, data leakage, excessive permissions, and AI workflow vulnerabilities. 

Prompt injection validation Data leakage assessment AI workflow vulnerability analysis

Prioritize 

Recommend

Provide practical remediation guidance and AI security best practices. 

Remediation guidance AI security best practices Risk reduction recommendations

Integrat 

Verify

Retest implemented controls to confirm vulnerabilities have been mitigated. 

Security control retesting Vulnerability mitigation validation Remediation effectiveness checks
Improve 

Improve

Continuously strengthen AI security as applications, models, and business requirements evolve. 

Continuous AI security enhancement Model governance optimization Ongoing risk management

WHY AI SECURITY REQUIRES A NEW APPROACH

Traditional application security focuses on software vulnerabilities. AI applications introduce new risks including prompt manipulation, sensitive information disclosure, excessive autonomy, insecure integrations, and AI-specific attack techniques. 

WHAT A MODERN AI SECURITY PROGRAM SHOULD DELIVER

BUSINESS OUTCOMES

Eliminate Security Blind Spots

Gain complete visibility into every connected asset across enterprise and operational environments.

Strengthen Cyber Resilience

Understand operational risks before attackers exploit hidden assets.

Improve Incident Response

Provide security teams with accurate asset intelligence during investigations.

Simplify Compliance

Maintain continuously updated asset inventories that support regulatory requirements.

Reduce Operational Risk

Identify vulnerable or unmanaged devices before they impact production.

Support Zero Trust

Build identity and segmentation strategies using accurate, real-time asset intelligence.

INDUSTRY APPLICATIONS

Manufacturing

Oil & Gas

Utilities & Energy

Healthcare

Transportation & Logistics

Smart Buildings & Campuses

OUR PERSPECTIVE

AI security cannot be treated as an extension of traditional application security. 

Large Language Models introduce entirely new attack vectors that require dedicated testing, governance, and continuous validation. At Mechsoft, we help organizations build AI securely by combining AI security assessments, OWASP LLM testing, and practical remediation guidance. The goal is not simply to deploy AI, but to deploy AI that the business can trust. 

FREQUENTLY ASKED QUESTIONS

It is an industry-recognized framework that identifies the most critical security risks affecting Large Language Model applications and Generative AI systems. 

LLMs introduce risks such as prompt injection, sensitive information disclosure, excessive permissions, insecure integrations, and AI-specific attack techniques that are not covered by traditional application security testing. 

Prompt injection is an attack where malicious instructions manipulate an AI model into ignoring its intended behaviour or exposing sensitive information. 

Yes. AI applications can be assessed using AI security testing, red teaming, prompt injection testing, API validation, and workflow security assessments designed specifically for LLMs. 

No. Enterprise AI assistants, internal copilots, Retrieval-Augmented Generation (RAG) systems, AI agents, and privately hosted LLMs all require security validation. 

AI security should be validated continuously as models, prompts, integrations, plugins, and business workflows evolve to ensure new risks are identified before they can be exploited. 

BUILD TRUST INTO EVERY AI APPLICATION

Secure your AI initiatives with comprehensive OWASP LLM security assessments that identify vulnerabilities, strengthen governance, and enable responsible AI adoption.

Whether you're deploying AI assistants, enterprise copilots, customer-facing chatbots, or autonomous AI workflows, Mechsoft helps you build AI systems that are secure, resilient, and ready for enterprise use.

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