Every AI Component. Every Exposure Point. Every Risk Quantified.

A systematic enumeration of your entire AI attack surface - models, agents, APIs, data pipelines - with risk scoring and an actionable remediation roadmap.

Duration: 1-2 weeks Team: 1 Senior AI Security Researcher
The Challenge

You might be experiencing...

You've deployed multiple AI models and agents but have no consolidated inventory of what's running, what it can access, or what it exposes.
Third-party models from Hugging Face, API providers, or SaaS vendors are integrated into your stack - but no one has assessed what they can do or leak.
Your AI agents have been granted tool permissions that have never been reviewed against least-privilege principles.
An enterprise customer or auditor is asking for your AI risk register, and your team has no structured framework to produce one.
New AI features ship regularly, but there is no security review gate - your attack surface grows with every release.

AI attack surface assessment is the necessary first step before any AI security testing program. You cannot pentest what you haven’t mapped. You cannot remediate what you haven’t quantified. And in most organizations deploying AI rapidly, the attack surface is significantly larger than anyone has documented.

Why AI Attack Surfaces Are Different

Traditional IT attack surfaces are built from known primitives: servers, applications, APIs, network segments. Security teams have mature tools and frameworks for enumerating these components.

AI attack surfaces introduce categories that traditional security tools were never designed to find:

  • Autonomous agents with tool permissions that can read files, call APIs, execute code, send emails, and take actions - often with permissions granted once and never reviewed
  • Third-party foundation models accessed via API, where your data is processed by infrastructure you don’t control and can’t inspect
  • RAG pipelines and vector databases that combine your proprietary data with model context in ways that can enable information disclosure
  • Prompt injection surfaces wherever your agents read external data - web pages, documents, emails, database records - that could contain adversarial instructions
  • Model supply chain dependencies where fine-tuned models, plugins, and datasets from external sources could contain vulnerabilities or malicious modifications

The Hidden AI Inventory Problem

Most organizations underestimate their AI asset inventory by a factor of two to five. Shadow AI deployments - LLMs connected by individual developers or teams without central IT involvement - are common. API integrations with AI providers are often set up quickly and forgotten. Agent permissions accumulate over time as new capabilities are added without removing old ones.

Our AI attack surface assessment enumerates all of this systematically. We combine stakeholder interviews, documentation review, network enumeration, and API discovery to build the most complete picture of your AI footprint that your team has ever seen.

From Assessment to Remediation

The output is not a vulnerability report - it is a risk register. Every finding is scored using the AIRS framework, giving your team a clear priority order for remediation. The remediation roadmap includes effort estimates so engineering leadership can plan sprints, not just receive a list of problems.

For organizations beginning an AI security program, the attack surface assessment is the correct starting point. It tells you what to test next, what requires immediate attention, and what your board-level AI risk exposure actually is.

Our Approach

Engagement Phases

Days 1-2

Discovery

Stakeholder interviews, documentation review, and automated enumeration to identify every AI component - models, APIs, agents, data pipelines, tool integrations, and third-party AI services in your stack.

Days 3-5

Enumeration

Deep mapping of each component: input/output paths, tool permissions, data access scope, model provenance, API exposure, and integration dependencies. Agent privilege scope diagrams produced.

Days 6-8

Risk Scoring

Each component scored using the AI Risk Surface (AIRS) framework - combining exploitability, blast radius, data sensitivity, and compliance relevance into a single risk score for prioritization.

Days 9-10

Reporting

Findings consolidated into an executive summary, technical risk register, and prioritized remediation roadmap. Walkthrough session scheduled with your team.

What You Get

Deliverables

Complete AI asset inventory with component classification and ownership
Attack surface map showing all exposure points, data flows, and trust boundaries
Risk register with AIRS scoring - each finding ranked by exploitability and blast radius
Agent privilege scope diagrams for all AI agent components
Executive summary for CISO and board-level reporting
1-hour findings walkthrough with your engineering and security leadership
Prioritized remediation roadmap with effort estimates
Expected Outcomes

Before & After

MetricBeforeAfter
AI Asset VisibilityUnknown - no consolidated AI inventory existsComplete asset inventory with risk scores in 1-2 weeks
Risk RegisterNo AI-specific risk register for auditors or boardPrioritized AIRS-scored risk register ready for compliance reporting
Remediation ClarityNo structured way to prioritize AI security improvementsRoadmap with effort estimates and risk-reduction impact per item
Technology

Tools We Use

MITRE ATLAS Custom AI scanning tools Model inventory frameworks OWASP LLM Top 10 AI Risk Surface (AIRS) scoring
Common Questions

Frequently Asked Questions

How is an AI attack surface assessment different from a traditional penetration test?

A traditional penetration test focuses on exploiting specific vulnerabilities in defined systems. An AI attack surface assessment maps your entire AI ecosystem first - identifying every component, data flow, permission boundary, and exposure point - before any exploitation occurs. Think of it as reconnaissance before a pentest. Many organizations have never done this for their AI stack and discover significant unknown assets and permissions in the process.

What AI components does the assessment cover?

We assess every AI component in your stack: proprietary and third-party LLMs, fine-tuned models, AI agents and orchestrators, RAG pipelines and vector databases, model APIs, AI SaaS integrations, training data pipelines, model serving infrastructure, and any tool integrations your agents use. If it processes AI input or produces AI output, it's in scope.

What is AIRS scoring?

The AI Risk Surface (AIRS) scoring framework combines four dimensions for each finding: exploitability (how hard is this to exploit?), blast radius (what's the maximum impact?), data sensitivity (what data is at risk?), and compliance relevance (does this create regulatory exposure?). The composite score drives prioritization - so your team fixes the highest-risk items first.

How long does the assessment take?

For most organizations, the engagement runs 1-2 weeks. Timeline depends on the number of AI components in scope, documentation availability, and stakeholder interview scheduling. Organizations with larger AI portfolios or limited documentation typically need the full two weeks.

What preparation is required from our team?

We need a technical point of contact who can answer questions about your AI stack, access to architecture documentation (if it exists), and brief stakeholder interviews with AI product owners and engineers. We do not require source code access - the assessment is conducted at the component and API level.

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