AI Security Consulting

Work directly with a senior cybersecurity consultant to review AI architecture, data flows and risks. The scope focuses on LLM applications, agents, prompt injection, data leakage and supply-chain exposure, with practical recommendations for SMEs and mid-sized businesses in Spain.

AI Security Services Use Cases We Cover

• AI architecture and threat modelling: identify assets, trust boundaries and realistic misuse scenarios.
• LLM and agent security: review prompt injection, permissions, output handling and data exposure within an agreed scope.
• AI supply-chain review: assess model and dependency provenance, provider choices and deployment controls.
• Data protection: review access, retention and leakage risks in prompts, retrieval, training and inference.
• Model and intellectual-property risks: review access to artifacts and endpoints and recommend proportionate controls.

Why Choose MicroHackers for AI Security?

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

Scoped technical review: agree authorized systems, test scenarios and limits before starting. Advanced model red teaming requires a separate capability assessment and explicit scope.

Practical reporting: receive prioritized findings, control recommendations and relevant framework references. A security assessment supports your compliance work; it does not certify the organization or guarantee compliance.

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

AI Threat Modelling and Architecture Review

AI systems introduce risks across models, data, applications and integrations. We review the architecture, entry points and trust boundaries, then prioritize practical controls with your team. Specialist testing of model evasion, poisoning, inversion or extraction is excluded unless capability, methods and scope have been separately assessed and agreed.

LLM Security Testing: Prompt Injection, Jailbreaks and Data Leakage

LLM applications can treat untrusted documents, messages or retrieved content as instructions. We review direct and indirect prompt injection, tool permissions, data access and output handling in the agreed application. Technical checks and evidence requirements are defined before testing; the work does not guarantee resistance to every jailbreak or misuse scenario.

AI Supply Chain Audits

We review the documented models, providers and dependencies used by your application, their provenance and the permissions of connected tools. The depth of the review depends on the access and evidence available. Findings identify supply-chain exposure and improvements; artifact verification or a detailed AI bill of materials is included only when agreed.

Data Protection in AI and RAG Pipelines

For AI and retrieval-augmented generation (RAG) workflows, we review which data enters the system, whether access controls survive retrieval, and how prompts, outputs and logs are handled. We identify excessive access, leakage and retention risks and recommend controls appropriate to the data and use case.

Model Privacy and IP Protection

We review how model artifacts and inference endpoints are accessed, stored and exposed, and whether responses reveal sensitive information. Recommendations may cover permissions, rate limits, logging and supplier controls. Preventing model cloning or extraction is not a guaranteed result of this consulting service.

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 identify the AI systems included in the engagement: models, applications, agents, third-party features, data access and owners. The agreed inventory and evidence provide the starting point for the review.

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. Scoped Technical Review

We perform the architecture review and technical checks agreed for the authorized systems. Findings distinguish observed weaknesses from hypotheses and document the evidence and limitations. Tests requiring additional specialist capacity are assessed separately before they are offered.

4. Report, Remediation and Re-test

Deliverables are agreed before starting and can include an executive summary, technical findings, supporting evidence and prioritized recommendations. Remediation support and re-testing, including timing and coverage, are included only when explicitly agreed.

The EU AI Act: Check Your Role, Use Case and Applicable Dates

The EU AI Act applies in stages and was amended by Regulation (EU) 2026/1744. Applicable duties depend on your role, the system and its use. Check the current consolidated text and the official implementation timeline when planning compliance.

• AI literacy: the 2026 amendment simplified the framework. Review the current Article 4 and plan appropriate understanding of AI risks for the people involved.
• Prohibited practices (Article 5) have applied since 2 February 2025.
• General-purpose AI model obligations have applied since 2 August 2025.
• Transparency requirements differ between informing people about AI interactions and marking synthetic content. The amended timetable includes a transition to 2 December 2026 for certain content-marking obligations; check the conditions applicable to your system.
• 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.

Start by mapping the systems you use, your role and the obligations relevant to each case. The security review can support risk analysis, access controls, logging and technical evidence. Legal classification and conformity assessment require the appropriate case-specific review.

Official references, checked 23 October 2026: European Commission AI Act timeline and consolidated AI Act text.

AI Security Consulting FAQs

What does an AI security consultant actually do?

An AI security consultant reviews the architecture, data flows, permissions and misuse risks of AI and LLM applications. Agreed technical checks and evidence support prioritized control recommendations. The scope and required specialist capabilities should be explicit before the engagement.

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. When using third-party AI, review what data you share, the permissions of integrations, supplier settings and how outputs are used. The provider secures its own platform; your organization still needs controls over its configuration, connected data and business processes.

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

An assessment can contribute technical findings and evidence to your control improvement, certification preparation and customer questionnaires. Any mapping to ENS, ISO 27001, NIST or AI Act requirements is agreed for the engagement; it does not replace a certification audit or legal assessment.

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.