AI Security Consulting

AI is powerful, but vulnerable. At MicroHackers, we specialize in securing AI systems against adversarial attacks, data leakage, and compliance risks. From LLM jailbreaks to AI supply chain vulnerabilities, we protect your models, data pipelines, and decision making processes.

AI Security Services Use Cases We Cover

• ✅ Adversarial Attack Protection: Prevent model evasion, poisoning and data leakage.
• ✅ LLM Security Testing: Evaluate and mitigate jailbreaks, prompt injection and hallucination risks.
• ✅ AI Supply Chain Audits: Assess open-source model dependencies, weights integrity and deployment security.
• ✅ Data Protection in AI Pipelines: Secure sensitive training and inference data.
• ✅ Model Privacy & IP Protection: Prevent reverse-engineering or unauthorized cloning of models.

Why Choose MicroHackers for AI Security?

🔐 Security-first AI Consulting: Specialized in threat modeling and hardening of AI components.

🧪 Red Team & Adversarial Testing: Simulate real-world attacks to discover and patch vulnerabilities.

📊 Compliance-ready Reports: Deliver actionable security assessments aligned with NIST, ISO/IEC 27001, and future EU AI Act requirements.

Who Needs an AI Security Consultant

Most organisations do not decide to adopt AI. It arrives. A support chatbot goes live, a sales team wires a copilot into the CRM, an engineer ships a RAG assistant over the internal document store, and a procurement platform starts scoring suppliers with a model nobody has reviewed. Within months there is a production AI surface that never passed through a security gate.

Our AI security consulting is built for that reality. It is designed for small and mid-sized companies putting large language models, copilots and machine learning into production faster than their security function can keep up, and that need an outside specialist rather than a full-time hire.

• Companies deploying LLM assistants, copilots or agents that touch customer data, internal documents or business systems.
• SaaS and technology vendors whose customers have started asking how their AI features are secured before they sign.
• Regulated and supply-chain-exposed businesses that must show due diligence over the AI they build or buy.
• Teams integrating third-party models and open-source weights without a process to assess what they are importing.
• Organisations with no in-house AI security expertise that need an independent assessment they can act on.

How We Secure Each Layer of Your AI Stack

Adversarial Attack Protection

Machine learning models fail in ways traditional applications do not. Carefully crafted inputs can flip a classification, poisoned training data can plant behaviour that only triggers under specific conditions, and repeated queries can reconstruct the data a model was trained on. We test your models against evasion, poisoning, model inversion and membership inference, then work with your team on the controls that actually reduce exposure: input validation, training data provenance, monitoring for anomalous query patterns and rate limiting on inference endpoints.

LLM Security Testing: Prompt Injection, Jailbreaks and Data Leakage

Prompt injection is the defining vulnerability class of LLM applications, and it does not stop at the chat box. Indirect injection hides instructions inside a web page, an email, a PDF or a support ticket that your assistant later reads, turning retrieved content into an attack channel. We test your deployment for direct and indirect prompt injection, jailbreaks that bypass system instructions, system prompt extraction, insecure output handling that leads to XSS or command execution downstream, and excessive agency where the model can trigger actions it should never be able to reach on its own.

AI Supply Chain Audits

An AI application is assembled from parts you did not build: base models, fine-tuned weights pulled from public hubs, vector databases, orchestration frameworks, plugins and agent tools, and a long tail of dependencies. Any of them can carry malicious code, unsafe deserialisation or a licence that conflicts with how you intend to use the output. We map that dependency graph, check the integrity and provenance of model artifacts, review the trust boundaries of plugins and tools, and give you an AI bill of materials you can maintain.

Data Protection in AI and RAG Pipelines

Retrieval-augmented generation is the fastest route to a data breach that nobody classifies as one. If your vector store ingests everything on a shared drive and your assistant answers every employee identically, the model has quietly become a permissions bypass. We review ingestion, embedding, retrieval and logging end to end: what data enters the index, whether document-level access control survives retrieval, how prompts and completions are stored and who can read them, and where personal data is being processed in ways your privacy notice does not describe.

Model Privacy and IP Protection

If a model represents real investment, it is an asset worth stealing. Exposed inference endpoints allow systematic querying to distil a competing model, unprotected artifacts can be lifted wholesale, and verbose responses can reveal proprietary logic and system design. We assess how your models are served, stored and versioned, and define the access, throttling, watermarking and monitoring controls that make extraction expensive and detectable.

Our AI Security Consulting Process

We follow the same structured engagement model we use across our other cybersecurity work, adapted to AI systems. The technical testing discipline comes from our penetration testing practice, and the governance and reporting side from our virtual CISO engagements.

1. Discovery and AI Asset Inventory

We start by finding out what you actually run. Models in production and in pilot, the applications and agents built on them, third-party AI features embedded in tools you already licence, the data each one can reach, and who owns them. Most engagements surface AI systems the security team did not know existed. The output is an inventory you keep, not a slide.

2. Threat Modelling

For each system that matters, we map trust boundaries, entry points, downstream actions and blast radius, and rank the realistic attack paths against your business impact. This is where we separate the vulnerabilities worth spending money on from the ones that make good conference talks. Work is aligned with the OWASP Top 10 for LLM Applications, the NIST AI Risk Management Framework and ISO/IEC 27001 controls.

3. Adversarial Testing

We attack the systems, manually and with tooling, from the position of a real adversary: an anonymous user, an authenticated customer, a malicious document entering the pipeline, a compromised dependency. Every finding is reproduced and evidenced so your engineers can confirm it without taking our word for it.

4. Report, Remediation and Re-test

You receive a technical report with reproducible findings and concrete fixes, plus an executive summary written for a board or a customer security questionnaire, mapped to the frameworks your auditors and clients ask about. We walk your team through remediation and re-test the fixes so the report closes rather than ages.

The EU AI Act Is Already Applying to You

Regulation (EU) 2024/1689, the EU AI Act, has been in force since 1 August 2024 and is applying in stages. Two of those stages are live today and are routinely missed by companies that assume the whole regime is still years away.

• AI literacy (Article 4) has applied since 2 February 2025. Providers and deployers must ensure the people operating AI systems on their behalf have an adequate level of understanding of them.
• Prohibited practices (Article 5) have applied since 2 February 2025.
• General-purpose AI model obligations have applied since 2 August 2025.
• Transparency obligations (Article 50) have applied since 2 August 2026. People must be told when they are interacting with an AI system, and synthetic audio, image, video and text must be marked in a machine-readable format.
• High-risk systems were deferred by the Digital Omnibus, Regulation (EU) 2026/1744, in force since 27 July 2026: stand-alone Annex III systems now apply from 2 December 2027, and Annex I systems embedded in regulated products from 2 August 2028.

The practical consequence is a window, not a reprieve. The transparency and literacy duties bite now, and the high-risk regime arriving in December 2027 requires risk management, data governance, logging, human oversight and technical documentation that cannot be assembled in the final quarter. We help you establish which category each of your systems falls into, close the obligations that already apply, and build the evidence base for the ones that are coming.

AI Security Consulting FAQs

What does an AI security consultant actually do?

An AI security consultant identifies how your machine learning and LLM systems can be attacked or abused, tests them adversarially, and defines the controls that reduce that risk. It covers the model, the data pipelines feeding it, the application and agents built around it, and the governance and compliance evidence you need to show customers and regulators.

How is AI security testing different from a normal penetration test?

A conventional penetration test targets deterministic systems with known failure modes. AI systems are probabilistic, and the same input can behave differently across runs. The attack surface also includes the model itself and everything it ingests: training data, retrieved documents, tool outputs and user prompts. Testing an LLM application means treating untrusted content as executable instruction, which is not part of a standard web application methodology.

We only use third-party AI like ChatGPT or Copilot. Do we still need this?

Yes, and this is the most common case we see. You are not responsible for securing the vendor model, but you are responsible for what you connect it to, what data leaves your organisation, what permissions your integrations hold, and the obligations that fall on you as a deployer under the AI Act. Shadow AI usage across a company is typically wider than management assumes.

Does an AI security assessment help with ISO 27001, ENS or customer questionnaires?

It does. Findings and evidence are mapped to ISO/IEC 27001, the NIST AI Risk Management Framework and the relevant AI Act articles, so the same work feeds your certification effort, your public-sector compliance and the security questionnaires that block deals. Companies increasingly find AI-specific questions in vendor due diligence with no documentation to answer them.

How does this connect with your other services?

AI security rarely stands alone. An assessment often leads into a virtual CISO engagement to run the governance side, penetration testing for the surrounding infrastructure and applications, and a review of your cyber insurance position, since insurers have begun asking specific questions about AI deployment.