AI Security & Guardrails

Secure the AI you already run.Before someone else tests it for you.

We review, harden and monitor AI applications and agents: what they can see, what they can do, and what happens when someone tries to misuse them. Practical engineering, aligned with the frameworks your customers and auditors ask about.

See what we check

At a glance

First step
Assess · 1–2 weeks
Who does the work
Senior engineers, hands on
Ownership
Your code, your IP
Ways to engage
Project, team or support
  • You own all code and IP
  • NDA before we start
  • 24/7 support available
  • Microsoft certified engineers

Why it matters now

  • AI now takes actions

    Agents call APIs, query databases and send messages. A manipulated prompt is no longer just a bad answer; it can be a real action in a real system.

  • Attacks hide in content

    Prompt injection can arrive through a document, an email or a web page your assistant reads. It is the number one risk in the OWASP Top 10 for LLM Applications.

  • Customers and regulators ask

    Enterprise buyers send AI security questionnaires, and the EU AI Act phases in obligations from 2025 to 2027. You need clear answers and evidence.

What we check

Every assessment covers the OWASP Top 10 for LLM Applications, with a concrete control for each risk.

OWASP Top 10 for LLM Applications (2025)

  • LLM01Prompt injectionInput and content filtering, isolation of untrusted content, Prompt Shields
  • LLM02Sensitive information disclosureData masking, permission-aware retrieval, output checks
  • LLM03Supply chainApproved models and packages, version pinning, provenance checks
  • LLM04Data and model poisoningControlled data sources, ingestion validation, change review
  • LLM05Improper output handlingTreat output as untrusted: validate, encode and sandbox
  • LLM06Excessive agencyLeast-privilege tools, scoped credentials, human approval for actions
  • LLM07System prompt leakageNo secrets in prompts, server-side policy enforcement
  • LLM08Vector and embedding weaknessesPer-tenant and per-user access control on retrieval
  • LLM09MisinformationGrounded answers with citations, evaluation against known answers
  • LLM10Unbounded consumptionRate limits, token budgets, cost alerts

Proof: related client work

Real engagements where we did this work, with the details.

How we work

  1. 011–2 weeks

    Assess

    Map your AI features, data flows, tools and permissions. Test them against the checklist and report risks in order of impact.

  2. 022–4 weeks

    Harden

    Fix what matters first: guardrails, access control, approval steps for sensitive actions, and safe handling of untrusted content.

  3. 03Ongoing

    Test

    Red-team the system with realistic attacks, and add security and quality evaluations to CI so every model or prompt change is checked.

  4. 04Ongoing

    Monitor

    Track usage, tool actions, costs and policy violations in production, with alerts and an audit trail you can show to customers.

What you get

  • Risk report, prioritised by impact
  • Architecture and data-flow review
  • Guardrail and access-control changes, implemented
  • Attack and evaluation test suite in your CI/CD
  • Monitoring dashboards and alerts
  • Evidence pack for customer security questionnaires

Tools we use

  • Azure AI Content Safety
  • Prompt Shields
  • Microsoft Entra ID
  • Microsoft Purview
  • Azure Key Vault
  • PyRIT
  • OpenTelemetry
  • Arize Phoenix

Frameworks we align with

OWASP Top 10 for LLM Applications (2025)
The checklist behind every assessment.
NIST AI Risk Management Framework
Including the Generative AI profile (NIST AI 600-1).
ISO/IEC 42001:2023
Controls and evidence that support your AI management system.
EU AI Act
Practical preparation as obligations phase in from 2025 to 2027.
MITRE ATLAS
Known adversary techniques against AI systems, used in red-teaming.

Ways to work with us

  • For a clear, defined goal

    Fixed-scope project

    Agreed scope, milestones and price before we start.

  • For ongoing product work

    Dedicated team

    Engineers who work as part of your team and scale with your roadmap.

  • For systems in production

    Support agreement

    A monthly block of hours for maintenance and improvements, with agreed response times.

Questions

Do you need access to our production data?

No. We start from architecture, code and a test environment, under your NDA. If production access helps, it is read-only, time-limited and agreed with you in advance.

Can you work with models other than Azure OpenAI?

Yes. The same controls apply to GPT, Claude, Gemini, Llama and Mistral models, whether hosted on Azure AI Foundry, Google Vertex AI, OCI or your own infrastructure.

Will this make us compliant with the EU AI Act or ISO/IEC 42001?

Compliance is decided by your legal team and auditors. We give you the engineering controls, tests and evidence those frameworks expect, so that conversation is short.

Find out where your AI stands.

A 30-minute call is enough to tell you what to check first, and how long it will take.