AI Security

Build AI features you can trust

evoila helps you secure the AI applications you are building, addressing the new risks that come with LLMs before they reach production.

Let’s talk about AI security

Security practice has not caught up yet. We help you close that gap.

Generative AI is moving into products faster than most security practices can follow. An LLM-powered feature introduces risks that traditional security tooling was never built to catch, including prompt injection, sensitive data leaking through model responses and abuse of a model’s access to internal systems. evoila helps engineering teams secure these applications as they move from prototype to production. This is a growing part of our portfolio, and we are clear about scope from the first conversation.

  • New AI-specific risks identified before features go live
  • Application-security rigor applied to LLM and AI workloads
  • An honest, practical starting point rather than hype

The failure modes are already being exploited in the wild

An AI feature that ships without security review is an attack surface most people in your organization do not yet understand.

The Challenge

New capabilities create a new attack surface

Teams are shipping AI features quickly, often connecting an LLM to internal data and tools to make it useful. That usefulness is also the risk. A model that can read documents or trigger actions can be manipulated into doing so against your interests. The failure modes are still unfamiliar to most security programs, while the pressure to release AI features leaves little room to think them through properly. That is how a new attack surface reaches production before the organization has caught up.

Prompt injection is real

Crafted input, sometimes hidden inside a document the model reads, overrides the developer’s instructions. Most current applications have no defense against it.

Sensitive data reaches responses

Model outputs can carry internal data further than intended. Sensitive content that enters the model has a way of surfacing again where it should not.

Agents with access can be manipulated

An AI agent that can query databases or trigger actions becomes a high-value target. A successful manipulation turns the model’s access into the attacker’s access.

Regulation is arriving on top of technical risk

The EU AI Act adds governance obligations to the technical challenge. Teams building AI features now need to track both dimensions at once.

The good news: AI security is not starting from zero

The discipline exists. The tooling exists. It needs to be applied to the right places in the application and connected to the governance model around it.

Our Solution

Application security for AI workloads

evoila approaches AI security as a focused extension of application security. We assess the AI features you are building, identify where the model introduces risk, and help you add the right safeguards before release. Our work draws on emerging industry guidance, including the OWASP Top 10 for LLM applications, and on the application-security discipline we already practice in DevSecOps.

AI risk assessment

We review your LLM application against known threats such as prompt injection, data exposure and unsafe model behavior before the feature goes live.

Secure design guidance

We advise on input handling, output filtering and how to scope a model’s access to tools and data so the feature stays useful without becoming too permissive.

Data protection

We reduce the chance of sensitive content reaching the model or surfacing again in responses by reviewing data flow, context boundaries and output handling.

Testing

We probe AI features for manipulation, unintended behavior and unsafe edge cases before they ship.

Governance link

Where governance matters, we connect technical safeguards to EU AI Act preparation through evoila’s GRC practice so the security controls and compliance obligations line up.

Tech-Deep-Dive

Where AI risk actually lives

Securing an AI application means looking at the model, its data and its connections to the rest of your systems.

The input boundary

Prompt injection is the signature AI risk: untrusted input, sometimes hidden in a document the model reads, overrides the developer’s instructions. We look at how your application separates trusted instructions from untrusted content and where input validation and guardrails belong.

The output boundary

Model responses can leak sensitive data or carry unsafe content into downstream systems. We review output filtering and how responses are handled once they leave the model.

Access and agency

When an LLM can call tools, query databases, or trigger actions, the impact of a successful manipulation grows. We assess how tightly the model’s permissions are scoped and whether high-impact actions require additional checks.

Method and standards

We align to emerging guidance such as the OWASP Top 10 for LLM applications and bring the same testing mindset used in our penetration testing and DevSecOps work. Where governance matters, we connect to EU AI Act preparation through our GRC practice.

Technical Advantages

Six things that get better before you ship

1. Injection resistance

Input handling and guardrails reviewed against prompt injection before launch.

2. Less data exposure

Output filtering and data scoping reduce the chance of sensitive content reaching model responses.

3. Scoped agent access

Model permissions to tools and data kept to the minimum needed for the feature.

4. Standards-based assessment

Aligned to OWASP guidance for LLM applications, not internal checklists.

5. Governance-ready safeguards

A direct bridge to EU AI Act obligations when your compliance timeline requires it.

6. Tested before it ships

Hands-on probing for manipulation and unintended behavior, not just documentation review.

Your partner of choice

Established discipline, applied to a new risk

AI security is new. The discipline behind it is not. evoila brings proven application-security and testing experience from DevSecOps and penetration testing and applies it to LLM-based applications. That is paired with a GRC practice already tracking the EU AI Act, so technical safeguards and governance obligations line up instead of drifting apart. evoila is deliberate about how this offering grows and clear about scope from the first conversation.

ISO 27001 certified

Our security practice meets an independently audited standard.

OWASP LLM Top 10 aligned

We assess against the recognized benchmark for LLM application risks.

DevSecOps and penetration testing practice

Hands-on testing experience, not just documentation review.

GRC practice for EU AI Act readiness

Technical safeguards and regulatory obligations covered in one place.

Technologies & Partners

Method-led, tool-agnostic

Our AI security work draws on the OWASP Top 10 for LLM applications and on the application-security tooling used in our DevSecOps practice. It connects to our penetration-testing capability for hands-on assessment and to our GRC practice for EU AI Act readiness. evoila is ISO 27001 certified.


Services & Starter Deals

Start focused. Scale as the use case grows.

1 | AI Security Assessment

A focused assessment of your AI feature, model interactions, data flows and user permissions. We identify the risks that matter first, including prompt injection, data exposure, unsafe outputs and excessive access.

2 | Secure-by-Design Advisory

Practical guidance for building security into the feature from the start – covering input and output controls, data boundaries, permissions, testing and governance requirements before release.

AI features bring risks your current tools were not built for.

Address them while the feature is still on the drawing board.

Let’s talk about AI security

The best AI security starts with a conversation. Get in Touch.

Tell us about the AI feature you are building, the systems it touches or the security questions already on your desk. We will help you identify where the real risk sits and what needs to be addressed first.

Patrick Cosic

Patrick Cosic

Business Unit Lead Security

FAQs

Commonly asked questions about AI Security

Prompt injection is when crafted input makes a language model ignore its intended instructions and follow the attacker’s instead. The input can even be hidden inside a document the model reads. It is the most common AI-specific risk, and we review how your application defends against it.

Yes. Most AI risks come from how the model is integrated, not just the model itself, including how you handle input, filter output, and scope its access to your data and systems. Those integration points are exactly where we focus.

It builds on the same discipline but adds risks unique to models, such as prompt injection, data leakage through responses, and manipulation of a model that can take actions. We apply established AppSec practice to these new failure modes.

This page addresses the technical security of AI applications. The EU AI Act is largely a governance and compliance matter, which we handle through our GRC practice. We connect the two so your safeguards and your regulatory obligations align.

We are honest that AI security is a growing part of our portfolio. We apply proven application-security and testing experience to it, and we scope engagements realistically, often starting with a focused assessment rather than a broad program.