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AI Security

Red-team your AI stack and detect adversarial threats before they reach production.

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What You Gain

  • A complete inventory of AI attack surfaces across your deployed systems, ranked by exploitability and business impact.
  • Documented prompt injection and adversarial input vulnerabilities, each with a reproduction proof-of-concept your developers can act on.
  • SIEM detection rules for AI-specific threat patterns, deployed and tuned to your environment.
  • A remediation roadmap that maps findings to your existing compliance obligations (Bill C-26, PIPEDA, sector-specific requirements).
  • Measurable reduction in mean time to detect AI-related anomalies, benchmarked before and after engagement.

What We Deliver

  • LLM Red-Team Report (executive summary + technical findings, OWASP LLM Top 10 mapped)
  • Adversarial prompt library used during testing (for internal awareness training)
  • AI-SOC detection content package (SIEM rules, playbooks, tuning notes)
  • AI supply chain risk register
  • Remediation roadmap with prioritised findings and owner assignments
  • Re-test attestation letter (critical findings only)

This Service Is Right for You If…

You have deployed a customer-facing or internal LLM application and have not had it independently tested.
Your SOC has no detection coverage for AI-specific threats (prompt injection, model API abuse, embedding exfiltration).
You handle sensitive data (health records, financial information, legal documents) in a RAG pipeline.
Your cyber insurance underwriter has asked about AI system security controls and you do not have a documented answer.
You are subject to Bill C-26 or operate in a federally regulated sector and your AI deployment has not been assessed for cyber resilience.

Frequently Asked Questions

What is prompt injection and why should my business care about it?

Prompt injection is an attack where a malicious instruction causes your AI system to ignore its original instructions and do something unauthorised. For businesses using AI to process requests or automate workflows, a successful injection can expose confidential data. It is the most commonly exploited LLM vulnerability in 2025.

We already do penetration testing. Does that cover our AI systems?

Standard penetration tests check for OWASP Top 10 web vulnerabilities and network misconfigurations. They do not test prompt injection, jailbreaks, model inversion, or adversarial input attacks. AI red-teaming requires different tooling, expertise, and a different threat model. The two assessments are complementary, not interchangeable.

How long does an AI red-team engagement take?

For a typical single LLM application with three to five integrations, active testing runs two to three weeks. Scoping takes one week. Report delivery follows within one week of testing completion. Total elapsed time is four to six weeks from kick-off to final deliverables.

Do you need access to our model weights or fine-tuning data?

Not necessarily. Mitiksha conducts black-box and grey-box assessments against deployed system endpoints, consistent with how real attackers operate. For higher-assurance engagements where training data privacy is a concern, we can extend to white-box review of fine-tuning datasets and model configuration.

Our AI vendor says their model is safe. Is a third-party assessment still necessary?

A model being safe in isolation and your deployed application being safe are different questions. Prompt injection risk lives in the application layer — how you construct prompts, what data you retrieve, what tools you expose. Even a well-aligned base model can be exploited through the surrounding application.

Does this service apply to Microsoft Copilot or other commercial AI products?

Yes. Commercial AI platforms introduce their own attack surface through plugin integrations, SharePoint retrieval, and enterprise connector configurations. Mitiksha assesses the deployment and configuration of commercial AI products against published vendor security guidance and known exploitation patterns.

Can you help us write security requirements for an AI system we are building?

Yes. Threat modelling and security requirements for AI systems in development is a natural precursor to red-teaming. Engaging before deployment is faster and less expensive than remediating after go-live. Contact us to discuss pre-launch security architecture review.

Last reviewed April 2026

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