Sekurzen
AI Security & Governance

Secure Your AI Before
Attackers Exploit It

AI moves fast. Security risks move faster. From LLM applications to deployed ML models, we test, harden, and govern your AI stack against prompt injection, data leakage, and adversarial attacks.

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3

specialized domains. AI app security, model integrity testing, and AI governance

OWASP

Top 10 for LLMs used as the foundation for all AI application security testing

End-to-End

coverage from API surface to model layer. not just a surface-level scan

What We Do

Three Pillars of AI Security

We go deep across your AI application stack, model infrastructure, and organizational governance. covering every layer attackers might target.

AI App Security

AI Application Security Testing

  • Prompt injection & jailbreaking attacks
  • PII and sensitive data leakage testing
  • Insecure output handling vulnerabilities
  • Retrieval-Augmented Generation (RAG) poisoning
  • Excessive agency & plugin misuse scenarios
  • LLM API surface and authentication review
  • OWASP Top 10 for LLMs coverage
Model Security

AI Security Testing

  • Model integrity & inversion attack resistance
  • Context poisoning and training data extraction
  • Adversarial input robustness testing
  • Model supply chain and dependency review
  • Fine-tuned model behavioral drift assessment
  • Inference endpoint hardening review
  • Privilege escalation via AI agent chaining
AI Governance

AI Governance Frameworks

  • AI risk register and threat landscape mapping
  • AI usage policy design and rollout
  • Responsible AI principles and guardrails
  • AI vendor and third-party risk assessment
  • Data classification for AI training and inputs
  • Employee AI security awareness training
  • Alignment with NIST AI RMF and EU AI Act
Our Process

How We Engage

A four-phase approach that maps your AI attack surface, executes targeted testing, delivers clear findings, and builds lasting governance.

1

Discover

Map all AI assets, LLM integrations, API surfaces, data flows, and model access points in scope.

2

Test

Execute adversarial attacks. prompt injection, jailbreaking, data extraction, and model abuse scenarios.

3

Report

Deliver detailed findings with severity ratings, proof-of-concept evidence, and prioritized remediation steps.

4

Govern

Implement governance frameworks, AI usage policies, and controls to sustain a secure AI posture over time.

Who This Is For

Built for Organizations Like Yours

🤖

AI-First Product Teams

Building LLM-powered applications, copilots, or RAG pipelines and need expert adversarial testing before launch or at scale.

  • LLM-powered features in production or pre-launch
  • Custom AI agents or multi-step pipelines
  • AI APIs exposed to external users
🏢

Enterprises Adopting AI Tools

Rolling out AI tools. internally or customer-facing. without a clear picture of the risks they introduce to your data and systems.

  • Microsoft Copilot or third-party AI tools deployed
  • Employees using AI for sensitive workflows
  • No formal AI security or usage policy in place
⚖️

Organizations Facing AI Compliance

Operating under regulatory requirements or customer expectations that demand demonstrable AI security and governance practices.

  • EU AI Act applicability under review
  • NIST AI RMF alignment required
  • Customers asking about AI risk management
Deliverables

What You Walk Away With

Every engagement produces a complete package. from technical testing artifacts to governance-ready documentation your leadership and auditors can act on.

✓
AI Application Penetration Test Report: detailed findings with severity, PoC evidence, and remediation guidance for every vulnerability discovered
✓
LLM Security Assessment: structured evaluation against the OWASP Top 10 for LLMs with gap analysis and risk ratings
✓
AI Risk Register: a living document cataloguing AI-specific risks, likelihood, impact, and ownership across your organization
✓
AI Governance Framework: policies, principles, and controls for responsible and secure AI deployment tailored to your organization
✓
AI Usage Policy: employee-ready guidelines covering approved AI tools, acceptable use, data handling, and prohibited actions
✓
Remediation Roadmap: prioritized action plan with short, medium, and long-term fixes aligned to your risk tolerance and resources
Client Story
"We thought our LLM app was secure because we had API authentication in place. SEKURZEN showed us twelve ways an attacker could bypass it, extract our training data, and manipulate outputs. all without touching the API key."
CTO, AI-Powered SaaS Platform

Before

  • No visibility into LLM app attack surface
  • Custom AI app shipped with no security testing
  • No AI usage policy for employees
  • Sensitive customer data in model context

After

  • 12 critical prompt injection vulns identified and fixed
  • PII leakage paths closed before customer impact
  • AI governance framework deployed org-wide
  • Team trained on secure AI development practices
FAQ

Common Questions

Prompt injection is an attack where a malicious user crafts input that overrides or hijacks the instructions given to an AI model. causing it to ignore safety guardrails, reveal confidential information, or take unintended actions. It is especially dangerous in agentic AI systems where the model has access to tools, APIs, or external data sources.
Yes. We test AI applications and models regardless of where they are hosted. Azure OpenAI, AWS Bedrock, Google Vertex AI, self-hosted open-source models (Llama, Mistral, etc.), or any custom fine-tuned model. Our testing methodology is model-agnostic and focuses on the application layer, API surface, and model behavior under adversarial conditions.
The OWASP Top 10 for LLM Applications is the industry-standard reference for the most critical security risks in AI systems. It covers threats including prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, and excessive agency. All our AI application security testing is mapped to this framework.
Yes. Microsoft 365 Copilot introduces new data exposure risks. it can surface files, emails, and SharePoint content that users may not realise are accessible. We assess your Copilot deployment for over-permissioned data, misconfigured sensitivity labels, and governance gaps, and help you implement the controls needed to use Copilot securely.
Yes. Our AI governance engagements include mapping your AI systems against the EU AI Act risk categories and NIST AI Risk Management Framework (AI RMF). We help you identify which systems fall under high-risk classifications, build the documentation required for compliance, and implement the technical and organizational controls needed to meet obligations.
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Get an expert assessment of your LLM applications, AI models, and governance posture before attackers find the gaps first.

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