AI Security Audit

WHOAMI’s AI Security Audit assesses the security of AI systems (models, pipelines, LLM applications, RAG, agents, and APIs) to identify weaknesses with operational impact: data exposure, feature abuse, incorrect decisions, fraud, audit risk, and trust degradation. You get a prioritized, actionable plan—no fluff and no empty checklists.

AI Security Audit Service in Spain

WHOAMI provides AI security audits in Spain for organizations integrating AI into products (chatbots, copilots, automation, scoring) or relying on third parties (external models, providers, integrations). We define a controlled scope and deliver defensible outcomes for leadership, risk, and engineering teams.

Security assessment for AI systems, models, and pipelines

To make this audit valuable, we treat “AI” as what it is in production: a socio‑technical system. We don’t assess only a model—we assess the full chain: data, identity, integrations, guardrails, observability, operations, and governance. The goal is reducing real risk without slowing delivery.

Objective and scope (what’s in, what’s out)

The objective is to identify weaknesses affecting confidentiality, integrity, availability, and traceability. Typical scope includes:

• AI applications: chat/assistants, internal copilots, automations, APIs
• LLMs / models: configuration, usage parameters, limits, output control
• RAG and knowledge: connectors, sources, permissions, access filtering
• Agents and tools: permissions, allowed actions, operational boundaries
• Data and prompts: minimization, PII handling, retention, redaction
• Identity and access: who can invoke what (users, services, third parties)
• Integrations: providers, external models, plugins, internal services
• Observability: logging, traces, evidence, alerts for abnormal usage

What we validate (and why it matters)

In AI security, value comes from mapping risks to impact. Examples:

• Data exposure: reduces the likelihood of sensitive information leaking via responses, logs, or connectors
• Permission control: prevents the AI system from accessing data/actions beyond the user’s role
• Feature abuse: lowers unwanted automations and anomalous use of endpoints/agents
• Result integrity: reduces harmful decisions caused by manipulated inputs or contaminated context
• Third‑party risk: improves control over providers, external models, and dependencies
• Traceability: enables investigation, explanation, and defensible evidence

Typical high‑value scenarios

• Chatbots with access to internal knowledge, tickets, CRM
• Internal copilots using enterprise data
• RAG connecting to documents, wikis, repositories, databases
• Agents capable of executing actions (tickets, changes, workflows)
• Scoring models influencing decisions (risk, fraud, prioritization)
• Regulated environments where evidence and control matter as much as accuracy

AI audit vs web/code audit

They complement each other with clear boundaries:

• AI: AI‑specific system risks (RAG, agents, data, guardrails, traceability, third parties)
• Web: runtime exposure of the application/APIs (Web Security Audit)
• Code: internal logic, dependencies, design (Source Code Audit)

For serious AI products, a layered approach (AI + web + code) is often the best fit depending on criticality—without mixing objectives.

How we work (high level)

• Kick‑off and system mapping: use cases, data, connectors, roles, boundaries
• Control and permissions review: identities, scopes, separation, knowledge access
• Key risk validation: data exposure, abuse, context integrity, traceability
• Prioritization and plan: quick wins + structural improvements (guardrails, governance, observability)

Deliverables (what you receive)

• Executive report (risk, impact, priorities, decisions)
• Technical report with evidence, context, actionable remediation guidance
• AI risk map (data exposure, control, integrity, third parties, traceability)
• 30/60/90 roadmap (quick wins, stabilization, structural improvements)
• Suggested backlog for engineering/product
• Review session to align implementation
• Follow‑up review (optional) to confirm critical improvements

What we need to start

• Use case description (what decisions the AI makes and what actions it can execute)
• Architecture (components, providers, connectors, data flows)
• Test access with representative roles (preferably non‑production)
• Knowledge sources (RAG) and access rules
• Policies (retention, privacy, security, logging) if available

How we prioritize

We prioritize by impact (data, continuity, reputation, audit/compliance), exposure (surface, connectors, roles), likelihood (current controls), and cost/benefit—so the plan is defensible and executable without harming the product.

Timelines and planning

It depends on use case count, connector complexity, and criticality. As a guideline:

• Scoped use case (one AI app + few sources): typically 2–3 weeks
• Mid‑size AI product (RAG + integrations): typically 3–6 weeks
• Complex platform (agents, multiple domains): phased by objectives

What this audit is NOT (service boundaries)

• Not a certification nor a guarantee of total security
• Not a paperwork‑only audit: we validate controls with technical evidence
• Not a how‑to guide: we describe risk and impact, not offensive recipes
• Not an abstract “AI ethics” review: it is technical and operational security of AI systems