Comprehensive Security Audit for AI Ecosystems

An adaptable approach combining offensive testing and deep technical review to ensure real and verifiable security.

Comprehensive and modular service designed to assess the security of an organization's AI components, both at the Platform level (MLOps/AI Cloud/Data Platforms) and in Integrations with traditional systems, LLM Models and their Orchestration, and Connectors/Plugins/Tools (MCPs) that enable agent capabilities with external services. The scope is configured “tailored” to the client’s context: only existing and in-use (or in deployment) modules are reviewed, maintaining methodological consistency between Ethical Hacking approaches (black/gray/white box, with different levels of internal knowledge) and Technical Auditing (with architectural detail, configurations, controls, and operational evidence provided by the client).

Module A

Technical Audit of AI Platforms
Review of configurations against best practices and vendor guidelines (ENISA, NIST AI RMF, CIS, OWASP AI Security, and provider recommendations). Covers training/inference/deployment infrastructure, data pipelines, monitoring, and governance.

Module B

Audit of Platforms with AI Integration
Evaluation of coupling points between legacy systems and AI components (web chatbots, image/text generation, RAG, third‑party APIs), focusing on data flow, authentication/authorization, isolation, content control, consent/personal data, and traceability. Includes prompt‑injection/jailbreak testing, context leakage, tool abuse, data/output poisoning, and provider dependency (SLA, retention, location).

Module C

LLM Security Assessment
Model and orchestration red‑teaming, with reproducible test suites (toxicity, bias, hallucination, context leakage), validation of policy adherence, robustness to prompt‑injection/jailbreak, review of controls (filters, RAG/grounding, rate‑limits, sandboxing) and telemetry (logging, traceability, feedback loops).

Module D

Security Audit of MCPs (connectors/plugins/tools)
Review of design and code of connectors enabling agents to interact with external services, covering authentication/authorization, input validation, secret management, rate control, isolation, sensitive data handling, and logging/traceability. Includes fuzzing, integration testing with the agent, dependency analysis, and threat modeling (SSRF, injection, deserialization, privilege escalation).

Applies to any environment that manages or consumes AI throughout its lifecycle, including:

Cloud Platforms and MLOps (Azure ML, AWS SageMaker, GCP Vertex, Databricks), on‑premise or hybrid tools, training/inference/deployment infrastructures, and data pipelines, monitoring, and governance.
AI Integrations in traditional platforms: Web/Mobile channels, chatbots, RAG, content generation, SDKs, connectors with data platforms, third‑party APIs, and operational controls (rate‑limits, moderation, prompt blocking, human review, telemetry).
LLMs and their ecosystem: model, orchestration layer, grounding/security controls, data management, telemetry, and governance.
Connectors/MCPs: connector/agent code, endpoints, security mechanisms, dependencies, and agent–backend interaction patterns.

The scope is adjusted per client: if a component does not exist, is not used, or is isolated, it is excluded or evaluated with depth proportional to risk and actual use.

Organizations that use or develop AI in production or are scaling capabilities, especially when integrations involve sensitive data, internal systems, or critical customer interaction. Relevant for sectors with high security requirements (finance/insurance, healthcare, industry, telecommunications, retail/e‑commerce, energy, public sector), teams with unaudited AI platforms, organizations with custom or fine‑tuned models, and environments subject to AI governance or regulatory obligations.

Reduced risk of data leakage, exposure of sensitive information, and model manipulation, protecting intellectual property and critical data.
Controls aligned with industry standards and guidelines, facilitating regulatory audits and internal reviews.
Improved reliability of the AI lifecycle (availability, resilience, and operational control).
Protection against novel attacks (prompt‑injection/jailbreak, tool abuse, poisoning, context leakage, agent pivoting) and mitigation of financial risk.
Technical evidence and traceability (logging, telemetry, and governance) useful for compliance, forensic investigation, and reducing exposure to sanctions.
Assessment of provider dependency (SLA, retention, location) and reduction of operational risk and future reprocessing costs.

Phase 1

Planning, discovery, and initial analysis
Understanding of architecture, inventory of AI components, use cases, data flows, trust boundaries, policies, and risk objectives; definition of the approach (black/gray/white box) and evidence required for technical auditing.

Phase 2

Technical evaluation and configuration/control review
Comparison against best practices (ENISA, NIST AI RMF, CIS, OWASP AI Security, and vendor guidelines), configuration/SDK review, access security, governance, personal data/consent management, and grounding/filter/rate‑limit/sandboxing controls.

Phase 3

Targeted testing and verification (according to applicable modules)
LLM red‑teaming and benchmarks, adversarial prompt‑injection/jailbreak scenarios, context leakage and tool abuse verification, integration testing, endpoint fuzzing, dependency analysis, and validation of telemetry/traceability and operational controls.

Phase 4

Executive and technical report + improvement roadmap
Findings prioritized by risk, evidence, recommendations, and remediation/strengthening plan per module, including operational and governance measures.

Executive Summary
- Executed scope (modules included/excluded and justification)
- Main risks and exposure (top findings)
- Maturity status and domain “heatmap” (platform, integration, LLM/orchestration, MCPs)
- Priority recommendations and “quick wins”
Scope and Methodology
- Evaluated architecture (high level), assumptions, limitations, and dependencies
- Testing approach (black/gray/white box) and technical auditing activities
- Reference frameworks (ENISA, NIST AI RMF, CIS, OWASP AI Security, vendor guidelines)
Technical Results by Modules (A–D)
- For each applicable module:
  - Reviewed surface (components, flows, accounts/roles, environments)
  - Findings with evidence and traceability (what, where, how it was verified)
  - Severity/criticality (impact/probability) and exploitation scenarios
  - Technical and operational recommendation (with acceptance criteria)
Action Plan
- Prioritized activities
- Recommendations for hardening, operational controls, and governance/telemetry
- Suggested “control owners” (platform, data, security, product)