AI Application Security
Set safer boundaries for AI-powered features.
Review the data, permissions and tool access behind your AI features so they stay useful without becoming a new way into your systems.
Tell us what you run. We'll help define the next step.
What we focus on
- Input handling and prompt injection
- Data access, retrieval and identity
- Tool permissions and output handling
- Scoped to your environment
- Clear deliverables, agreed upfront
- No account needed to enquire
AI Application Security Services
AI features change how an application behaves. A component that accepts open-ended input, retrieves supporting content, calls tools on a user's behalf and produces output that other systems act on introduces boundaries that traditional application testing was not designed to examine. The risk is rarely the model itself; it is what the model can reach, what it can trigger, and what happens to its output.
Our AI application security service is for product and engineering teams shipping assistants, chat interfaces, summarisation, document search, retrieval-augmented features and agent workflows. We help teams map the real data flow, separate trusted instruction from untrusted content, enforce the requesting user's permissions at retrieval time, scope tool access and approvals, and validate output before it is rendered, stored or forwarded. Work is scoped around your model provider and architecture rather than a particular vendor.
AI application security for prompts, tools and data boundaries
AI features change the shape of an application. Instead of a fixed set of routes and parameters, you now have a component that accepts open-ended input, pulls in retrieved content, calls tools on a user’s behalf and produces output that other systems act on. Each of those steps is a boundary worth designing deliberately.
We map what the feature can read, what it can do and where its output goes, then design the boundaries: separating instruction from content, enforcing the requesting user's permissions at retrieval, scoping tools and validating output. The work is about practical limits that reduce misuse without removing the value of the feature. We review your application and its integration; we do not audit or retrain the model itself.
The most common problems are not exotic. They are an assistant with more data access than the user asking the question, a tool permission that can change state without approval, retrieved content treated as trusted instruction, or model output written straight into a page or a database without validation.
Best fit for
- Products adding chat, summarisation, automation or AI agents
- Teams connecting AI features to private data or business tools
- Applications that need safer prompt, retrieval and permission design
What you receive
- A documented data flow and trust boundary map for AI features
- Prompt, retrieval and tool-permission recommendations
- Abuse cases, testing priorities and monitoring guidance
AI application security areas we help you plan
AI security is mostly application security applied to a component that treats text as instruction. The useful questions are about identity, permission, data access and what happens to the output, not about the model itself.
Input, prompt handling and injection
Anything the model reads can attempt to instruct it, including user messages, uploaded documents, retrieved records, web content and API responses. We review how system instructions are separated from untrusted content, how retrieved material is delimited and attributed, and where prompt injection would lead to a consequence rather than just an odd answer. The emphasis is on limiting what a successful injection can reach, because instruction filtering alone is not a dependable control, a position the OWASP Top 10 for Large Language Model Applications takes as well.
Data access, retrieval and user identity
The most damaging AI issues are usually authorisation problems in a new form. If a retrieval layer queries a shared index without applying the requesting user’s permissions, the assistant becomes a way to read records that user could never open directly. We review how identity flows into retrieval, how document-level permissions are enforced, what is embedded and where, and how sensitive fields are excluded or redacted before they reach the context window.
Tool permissions, output handling and abuse
When an AI feature can call tools, its permissions become the real security boundary. We review which actions are available, which change state or spend money, where human approval is required, and how the feature is scoped to the acting user rather than a shared service account. Output handling matters too: model text rendered into a page, passed to a shell, used in a query or fed to another agent needs the same validation and encoding as any other untrusted input.
A focused AI security scope before the feature ships.
Before recommending guardrails, we build a practical picture of what the AI feature can see, what it can do, and who is accountable for the actions it takes.
Feature behaviour and data flow
We map how the feature receives input, what context is assembled, which data sources are reachable, what the model returns and where that output goes next. This produces a diagram of trust boundaries that makes the risky steps visible rather than implied, and it usually surfaces data paths the team had not considered part of the feature.
Identity, permissions and tool access
We review how the requesting user’s identity reaches retrieval and tool calls, whether a shared service account is used, which tools can change state or incur cost, and where approval is required. Where an agent can chain actions, we look at the worst-case sequence rather than the intended one.
Abuse cases, testing and monitoring
We define the abuse cases worth testing for your feature, including prompt injection through retrieved content, attempts to extract system instructions or other users’ data, and consumption abuse. Alongside that we agree what should be logged, how much prompt and output content can be retained safely, and what monitoring would show misuse.
Start with the right scope.
Tell us what the AI feature does, which data it can reach, which tools it can call and where it is hosted. We will confirm the proposed scope, platform responsibilities, deliverables, availability and pricing before work begins.
What an AI application security engagement can include
- AI feature data-flow and trust boundary map
- Prompt, context assembly and instruction separation review
- Retrieval permission and data exposure assessment
- Tool permission, approval and credential scoping recommendations
- Output handling and downstream validation guidance
- Prompt injection and abuse-case test plan
- Logging, retention and monitoring recommendations for AI workflows
- Quote-ready scope with technology, effort and responsibilities defined
Request a quote for ai application security
Share the systems involved, your priorities and the tools you already use. We reply by email with the scope, availability and pricing once we have reviewed what you send.
Your enquiry is with our team.
We will review what you sent and reply to your email address. Nothing starts until scope and price are agreed in writing.
Explore services in the meantimePractical boundaries for AI features, without removing the value.
Data-flow mapping comes first and abuse testing last, so limits are placed where they change the outcome rather than where they are easiest to add.
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01
Feature and data-flow mapping
We document how input arrives, what context is assembled, which sources are reachable and where model output is used downstream.
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02
Boundary design
Instruction separation, retrieval permissions, tool scoping and approval requirements are designed around the feature’s actual capability and audience.
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03
Abuse testing
We test the abuse cases that matter for the feature, including injection through retrieved content, data probing and unintended tool use.
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04
Operational handoff
We document the boundaries, logging, retention position, monitoring signals and ownership for the team maintaining the feature.
Questions teams ask before shipping an AI feature.
Most AI security problems turn out to be familiar authorisation and input-handling issues in a new form. These answers explain how we approach them.
What is prompt injection and can it be filtered out?
Prompt injection is when content the model reads is treated as instruction, whether that content comes from a user message, an uploaded document, a retrieved record or a web page. OWASP lists it first in its Top 10 for Large Language Model Applications and notes that no foolproof method of prevention is known, which is why the practical defence is to limit what a successful injection can reach: enforce permissions at retrieval, scope tool access, require approval for consequential actions and validate output before anything acts on it.
Is our data used to train the model?
That depends entirely on the provider, the plan and the settings you have chosen, so it is confirmed against your contract rather than assumed. As part of the review we look at where prompts and outputs are sent, what is retained, what logging captures, and whether sensitive fields should be excluded or redacted before they ever leave your application.
Do we need this if we only use a hosted model through an API?
Usually yes, because most of the risk sits in your application rather than the model. How you assemble context, whose permissions apply at retrieval, which tools the feature can call and what happens to the output are all your decisions, and they are where the significant issues are typically found.
How is this different from a normal penetration test?
A standard test focuses on fixed routes, parameters and known vulnerability classes. AI features add open-ended input, retrieval over data the user may not be entitled to, tool execution and output that other systems trust. The review covers those specifically, and can be run alongside a conventional assessment of the surrounding application.
Can you review an agent that calls multiple tools?
Yes, and chained actions get particular attention. We look at the worst-case sequence rather than the intended one: which tools can be combined, whether the agent runs as the user or as a shared service account, where approval is required, and how far an unintended chain could travel before something stops it.
Does the review stay valid if we change the prompt, model or tools later?
Not indefinitely. Trust boundaries, permissions and abuse cases are reviewed against the system as it exists at the time. A materially different prompt, a new tool the assistant can call, or a switch to a model with different behaviour is worth a follow-up review, particularly where the assistant can take actions rather than only generate text.
Need a specific answer? Talk to our team
Further reading: OWASP Top 10 for Large Language Model Applications
Services that pair naturally with AI application security.
An AI feature sits inside an existing application and data estate. These services cover the adjacent risks that usually surface during the review.