Here’s something we see constantly: a company deploys an AI application — a customer-facing chatbot, an internal copilot, a RAG-powered knowledge base — and the security review consists of someone on the engineering team saying “we added guardrails” and everyone nodding.
Nobody tested those guardrails. Nobody tried to break them. Nobody checked whether the AI could be manipulated into leaking sensitive data, bypassing access controls, or generating outputs that create regulatory exposure.
Then six months later, a user discovers that a carefully worded prompt can extract internal pricing data from the customer support bot. Or an employee pastes a CUI-marked document into ChatGPT because nobody told them not to. Or your RAG system happily answers questions about HR termination records because the vector database doesn’t respect the access controls on the source documents.
These aren’t hypothetical scenarios. We see them in real assessments, at real companies, with real consequences.
Traditional security assessments — penetration tests, vulnerability scans, SOC 2 audits — don’t cover any of this. They were designed for a world where applications had predictable inputs, deterministic outputs, and well-defined attack surfaces. AI systems have none of those properties.
Why AI Introduces Security Risks Your Current Controls Don’t Catch
A traditional web application has a defined set of inputs: form fields, API parameters, URL routes. You can enumerate the attack surface. You can write validation rules. You can test exhaustively.
A language model accepts natural language. The input space is effectively infinite. The output is non-deterministic. And the model’s behavior can be manipulated through conversational techniques that look nothing like traditional exploits.
Here are the specific risk categories that fall outside traditional security coverage:
Prompt injection. An attacker crafts an input that causes the AI to ignore its system instructions, reveal its internal prompt, or execute unauthorized actions. Direct injection targets the model directly. Indirect injection hides malicious instructions in documents or web content the model processes. Most naive guardrails — simple keyword filters or instruction-based restrictions — fail within minutes against a motivated attacker.
Data leakage through LLM responses. Your AI system processes sensitive data to generate responses. Without proper controls, it can expose PII, proprietary information, CUI, or internal business data to users who shouldn’t have access. This is especially dangerous in RAG systems where the model has access to a broad document corpus.
Training data exposure. Models can memorize and regurgitate fragments of their training data. If you fine-tuned a model on sensitive information, that information can potentially be extracted through targeted prompting.
Shadow AI. Your employees are using AI tools you didn’t approve, didn’t configure, and don’t monitor. They’re pasting customer data into ChatGPT. They’re uploading financial models to Claude. They’re using AI coding assistants that send proprietary source code to third-party servers. You can’t secure what you can’t see.
RAG system data exposure. Retrieval-Augmented Generation systems give the AI access to your internal documents. If the retrieval layer doesn’t enforce the same access controls as your document management system, any user who can query the AI can access any document in the corpus — regardless of their clearance level.
API abuse. AI services exposed through APIs can be exploited for purposes beyond their intended scope — data extraction, model theft through repeated queries, or leveraging your API key for unauthorized usage.
Traditional penetration tests check whether an attacker can break into your systems. AI security assessments check whether an attacker can manipulate the systems you’ve already invited them to use.
What an AI Security Assessment Actually Evaluates
A proper AI security assessment isn’t a single test. It’s a structured evaluation across multiple domains, each targeting a different category of risk. Here’s what we cover in our AI Security & Red Team Testing engagements.
AI System Inventory
You can’t secure what you haven’t cataloged. The first step is identifying every AI system in your organization:
- LLM-powered applications (customer-facing and internal)
- AI agents and automation workflows
- Internal copilots and assistants
- RAG systems and knowledge bases
- Third-party AI tools employees are using (approved and unapproved)
- AI components embedded in vendor software
Most companies are surprised by this inventory. The official count is usually three or four AI applications. The actual count — including shadow AI, embedded vendor AI, and departmental experiments — is typically three to five times higher.
Outcome: A complete map of your AI attack surface.
Data Exposure Risk
This is where most of the serious findings live. We evaluate:
- What sensitive data flows into AI prompts and context windows
- Which documents are indexed in RAG systems and whether access controls carry through to AI outputs
- Vector database security — who can query it, what’s stored in embeddings, whether metadata leaks information
- Data retention in AI systems — conversation logs, cached responses, training data
- Cross-tenant isolation in multi-user AI applications
One common finding: a RAG system built on SharePoint where the service account has read access to the entire document library, effectively giving every user access to every document through the AI interface — even documents they couldn’t access directly.
Outcome: A clear picture of where sensitive information could leak through AI channels.
Model Security
We assess the AI models themselves and the infrastructure around them:
- Prompt injection resilience. We run 1,000+ automated attack vectors plus manual expert probing for domain-specific vulnerabilities. Direct injection, indirect injection, multi-turn escalation, encoding tricks, persona switching — the full adversarial toolkit.
- Jailbreak testing. Can the model be manipulated into ignoring safety guardrails? Can it generate harmful, off-brand, or legally problematic outputs?
- Access control bypass. Can users access data above their clearance level through conversational manipulation?
- API security. Are AI service endpoints properly authenticated, rate-limited, and monitored?
Outcome: A scored vulnerability report with reproduction steps for every finding.
AI Governance and Policy
Technical controls are half the picture. We also evaluate:
- Do you have an AI usage policy? Is it enforced?
- Is there an approval process for deploying new AI tools?
- Do employees know what data they can and can’t put into AI systems?
- How are third-party AI vendors evaluated for security and compliance?
- Is there a process for monitoring AI system behavior in production?
The governance gap is often the biggest risk. We frequently find organizations with well-secured production AI systems and zero controls around employee use of consumer AI tools.
Outcome: An honest assessment of your governance maturity and specific policy recommendations.
Infrastructure Security
AI systems run on infrastructure that needs its own security review:
- Cloud configuration for AI workloads (Azure OpenAI, AWS Bedrock, etc.)
- Network isolation between AI services and sensitive data stores
- API authentication and key management
- Logging, monitoring, and alerting for AI-specific events
- Model hosting environment hardening
Outcome: Secure architecture recommendations specific to your AI deployment.
Common Issues We Find in Real Assessments
Every environment is different, but certain findings come up repeatedly:
Employees pasting sensitive documents into consumer AI tools. This is nearly universal. Without clear policies and technical controls (DLP integration, approved AI tool provisioning), your confidential data is flowing to third-party AI providers. In ITAR or CUI environments, this can be a compliance violation — not just a risk.
RAG systems that don’t enforce document-level access controls. The retrieval layer pulls documents based on relevance, not permissions. A junior employee asks the AI a question, and the answer draws from board-level financial documents, HR records, or export-controlled technical data.
AI applications with unrestricted API keys. No rate limiting. No usage monitoring. No key rotation. One compromised key gives an attacker unlimited access to your AI services and, through them, to whatever data those services can access.
AI copilots with database access scoped too broadly. The copilot needs read access to answer questions. But instead of scoping access to specific tables and views, it has read access to the entire database. A clever user can extract data the copilot was never intended to expose.
No logging of AI interactions. If you can’t see what users are asking and what the AI is responding, you can’t detect misuse, investigate incidents, or demonstrate compliance. Yet many AI deployments ship with minimal or no logging.
Each of these represents a concrete business risk — regulatory penalties, data breaches, competitive intelligence exposure, or reputational damage.
What You Actually Receive
An AI security assessment isn’t a check-the-box exercise. You get deliverables your security team, engineering team, and leadership can act on.
Executive Risk Scorecard
A one-page pass/fail assessment across all tested categories. Board-ready. Your CISO can present it to leadership without translation. It answers the question: “Where do we stand on AI security right now?”
Detailed Vulnerability Report
Every finding documented with severity rating (Critical / High / Medium / Low), evidence, reproduction steps, and specific remediation guidance. No ambiguity about what’s broken or how to fix it.
AI Governance Framework
If your policies have gaps — and they usually do — you receive a practical governance framework covering acceptable AI use, model deployment approval processes, data protection requirements, and vendor risk management. Not a generic template. A framework tailored to your industry, your regulatory requirements, and your existing security program.
Remediation Roadmap
A prioritized fix list scored by effort and impact. Critical vulnerabilities with quick fixes first. Architectural recommendations for systemic issues. Timeline estimates for each remediation item.
Validation and Attestation
After your team implements fixes, we re-test to confirm vulnerabilities are resolved. You receive a security attestation documenting the tested scope, methodology, and results — useful for compliance audits, customer due diligence, and internal risk reporting.
The deliverable that matters most isn’t the report. It’s the remediation roadmap. A list of vulnerabilities without a path to fixing them is just anxiety on paper.
What Experienced Organizations Do Differently
Companies that take AI security seriously — the ones that don’t show up in breach notifications — share a few common practices:
They maintain an AI system registry. Every AI tool, every model, every integration is cataloged with its data access, risk classification, and responsible owner. New tools go through a review process before deployment.
They red team before production. AI applications get adversarial testing as part of the deployment pipeline, not as a one-time exercise after something goes wrong. Our AI Security & Red Team Testing engagements are designed to fit into this pre-deployment workflow.
They monitor AI systems in production. Prompt logs, response monitoring, usage analytics, and anomaly detection. Not just for security — also for quality, compliance, and cost management.
They treat AI governance as a security function. Not an innovation initiative. Not an IT project. A security and compliance function with clear ownership, defined processes, and executive visibility.
They plan for model updates. When your AI provider updates a model, your guardrails might break. Experienced organizations include model update regression testing in their security program.
These practices aren’t aspirational. They’re what OWASP, NIST AI RMF, and ISO 42001 recommend. The companies that implement them deploy AI faster and more confidently — because they’ve addressed the risks that slow everyone else down.
AI Security Is a Governance Problem, Not Just a Technical One
The companies most at risk aren’t the ones with weak technology. They’re the ones with no framework for evaluating AI risk in the first place.
An AI security assessment doesn’t just find vulnerabilities. It builds the foundation for governing AI responsibly — the policies, the processes, the monitoring, and the organizational muscle to deploy AI without creating the risks that keep CISOs up at night.
If you’re deploying AI in any environment that handles sensitive data — defense, manufacturing, financial services, healthcare, engineering — you should understand your AI risk posture before you scale. Not after the first incident.
Organizations exploring enterprise AI should start with an AI security assessment to understand where risk exists before scaling AI initiatives. Talk to our security team →
