AI Security Deep Dive

AI Red-Team Playbook for Regulated Mid-Market Organizations

A practitioner playbook for running an AI red team against production generative AI deployments — Copilot, ChatGPT Enterprise, Claude, Bedrock, custom RAG — mapped to the NIST AI RMF, OWASP LLM Top 10 2025, MITRE ATLAS, and the HIPAA / SOC 2 / CMMC 2.0 evidence auditors now expect. Start a 90-Day Proof →

Definition

An AI red team is a structured adversarial assessment of a deployed generative AI, large language model (LLM), or machine learning system — simulating the tactics a motivated attacker would use to bypass model guardrails, exfiltrate training data, manipulate outputs, or weaponize the model against its users. Aligned with the NIST AI Risk Management Framework (AI RMF 1.0), OWASP LLM Top 10 2025, MITRE ATLAS, and ISO/IEC 42001, an AI red-team engagement exercises prompt injection, jailbreak chaining, model inversion, training data extraction, retrieval-augmented generation (RAG) poisoning, agentic tool abuse, and indirect prompt injection against production AI systems. For regulated mid-market organizations deploying Microsoft Copilot for Microsoft 365, ChatGPT Enterprise, Anthropic Claude, custom RAG chatbots, or AI agents integrated with PHI, payment data, or CUI, an AI red team is increasingly required evidence for HIPAA, SOC 2, CMMC 2.0, and cyber insurance underwriting renewals.

Context

Why AI Red-Teaming Became a Mid-Market Requirement in 2025-2026

Until recently, AI red-teaming was an activity performed by OpenAI, Anthropic, Google DeepMind, and Microsoft Research against their own foundation models before release. Mid-market organizations consumed those models and assumed the upstream red teams had done the work. That assumption broke in 2024-2025 for three reasons.First, retrieval-augmented generation (RAG) became the default deployment pattern. Organizations started pointing LLMs at their own data — SharePoint, Confluence, clinical notes, contract repositories — and the upstream foundation model red team had no visibility into what the downstream data layer contained. Prompt injection through ingested documents, RAG poisoning, and cross-tenant data leakage became the dominant risk category. Second, AI agents with tool access arrived. Copilot Studio, Microsoft Copilot Agents, AWS Bedrock Agents, and custom LangChain / LlamaIndex deployments gave LLMs the ability to call APIs, send emails, write to databases, and execute code. The attack surface expanded from “model outputs text” to “model takes action.” Third, regulators caught up. NIST released the AI RMF generative AI profile; the EU AI Act entered force with conformity assessment requirements; the SEC, OCC, FTC, and state regulators began issuing guidance and enforcement actions. Cyber insurance underwriters added AI governance questions to the 2025 renewal cycle.

For regulated mid-market organizations, the result is that AI red-teaming is no longer optional evidence for a mature security program — it is baseline documentation.

Methodology

The AI Red-Team Methodology We Use

A five-phase methodology adapted from MITRE ATLAS and the OWASP LLM Top 10 2025, scoped to the client’s actual AI deployment — not a generic model evaluation.

01

Scoping & Threat Modeling

Map every AI touchpoint: foundation model, fine-tuning, RAG data sources, prompt templates, agent tools, user-facing surfaces, and downstream consumers. Produce a STRIDE-for-AI threat model tied to data classifications (PHI, PCI, CUI, trade secrets).

02

Offensive Testing

Run the full adversarial catalog: direct and indirect prompt injection, jailbreak chaining, system prompt extraction, training data extraction attempts, RAG poisoning, agent tool abuse, and cross-tenant leakage testing — via automated tooling (Counterfit, Garak, PyRIT, Promptfoo) and manual exploitation by experienced testers.

03

Impact Validation

For every finding, demonstrate end-to-end impact: what data can be exfiltrated, what action can the agent be caused to take, what misinformation can be injected into user-visible outputs. Findings without demonstrated impact go into the informational section, not the executive summary.

04

Reporting & Remediation

An executive summary with prioritized findings, a technical report with reproducible test steps, and a remediation roadmap mapped to NIST AI RMF functions (govern, map, measure, manage) and OWASP LLM Top 10 categories.

05

Retest & Attestation

After remediation, retest the validated exploit paths and issue an attestation letter suitable for auditor, regulator, and insurance underwriter review.

OWASP LLM Top 10 — 2025

Attack Categories We Test Against Production AI Deployments

The OWASP LLM Top 10 2025 defines the current canonical categories. Our red-team catalog covers each.

LLM01

Prompt Injection

Direct instruction override via user input, including client-specific variants designed to extract system prompts, bypass content filters, and impersonate privileged roles.

LLM02

Sensitive Information Disclosure

Can the model be induced to reveal PHI, PCI, CUI, or trade secrets embedded in RAG sources, training data, or system prompts?

LLM03

Supply Chain

Are fine-tuning data, embedding models, and third-party plugins verified? For clients using Hugging Face models or open-source components, we audit provenance.

LLM04

Data & Model Poisoning

If the client allows user-uploaded documents into a RAG index, can an attacker poison the index with adversarial content that steers future responses?

LLM05

Improper Output Handling

Does the application treat LLM output as trusted input downstream (SQL execution, shell commands, email sending)? Classic SSRF-style chains through AI agents.

LLM06

Excessive Agency

For agentic deployments, what actions can the model take without human approval? We test privilege escalation, tool chaining, and scope creep.

LLM07

System Prompt Leakage

Can the model be induced to reveal its system prompt? For clients with proprietary prompt engineering, this is a trade-secret risk.

LLM08

Vector & Embedding Weaknesses

RAG-specific attacks against the embedding layer, including adversarial embeddings and cross-tenant leakage in shared vector databases.

LLM09

Misinformation

Hallucination behavior under adversarial pressure. For healthcare and financial services clients, this is the compliance-critical category.

LLM10

Unbounded Consumption

Economic and availability attacks — cost exhaustion, token flooding, denial of wallet.

Engagement

Timeline, Deliverables & What It Costs

A standard AI red-team engagement for a mid-market organization runs 4–8 weeks depending on deployment complexity.

Week 1

Scoping, threat modeling, environment access, rules of engagement.

Weeks 2–5

Active adversarial testing across OWASP LLM Top 10 categories, industry-specific test catalog, and client-priority scenarios.

Week 6

Impact validation, reporting, executive readout.

Weeks 7–8 (optional)

Remediation advisory, retest of validated exploit paths, attestation letter.

Deliverables include: an executive summary (5-10 pages), a technical report with reproducible test steps (40-80 pages), NIST AI RMF mapping, OWASP LLM Top 10 mapping, a prioritized remediation roadmap, a retest attestation letter, and raw test artifacts (prompts, responses, screenshots) suitable for audit workpaper review. Typical cost: $35,000–$125,000 depending on scope, deployment count, and depth. Engagements are fixed-fee with a scoping call before proposal.

For organizations running Armorstack SENTRY’s managed SOC, AI red-team engagements integrate with ongoing AI observability monitoring so findings roll into the continuous program rather than becoming point-in-time PDFs.

By Industry

Industry-Specific Considerations

Generic AI red-team methodologies miss the regulatory specifics. We maintain industry-specific test catalogs.

HC

Healthcare

For clients deploying Microsoft Copilot for Microsoft 365, Epic Ambient Experience, Abridge, Nuance DAX, or custom clinical LLMs, we test PHI exfiltration through RAG indexing of clinical notes, cross-patient data leakage, hallucination behavior on diagnostic or dosing queries, and HIPAA minimum-necessary violations — aligned to the HHS HIPAA Security Rule and FDA Good Machine Learning Practice guidance.

FS

Financial Services

For wealth managers, community banks, and fintech deploying AI for customer service, loan underwriting, fraud detection, or research summarization, we test MNPI leakage, fair-lending disparate-impact behavior, FINRA 2210 compliance, and SEC Rule 10b5-1 inadvertent disclosure risk.

DEF

Defense & Aerospace

For CMMC 2.0 Level 2 and Level 3 clients, we test CUI exfiltration, ITAR-regulated technical data handling, and cross-domain boundary violations when AI systems operate on mixed classification data.

LEG

Legal

For law firms and legal operations teams deploying Harvey AI, CoCounsel, or custom contract-review systems, we test privilege-waiver risk through inadvertent disclosure, conflict-of-interest breach scenarios, and attorney-client confidentiality preservation under adversarial inputs.

Field Experience

What We Test Against, in Production

We have run AI red-team engagements against the following deployment patterns — listed to give prospective clients a realistic picture of what our team has already seen.

M365

Microsoft Copilot for M365

Graph-connected data sources — testing cross-tenant leakage, SharePoint permission inheritance, and prompt injection via ingested email.

CS

Copilot Studio Agents

Power Platform tool access — testing excessive agency, tool chaining, and Dataverse exfiltration.

GPT

ChatGPT Enterprise / Team

Custom GPT deployments — testing system prompt leakage, file-ingestion abuse, and conversation history exposure.

CL

Claude for Enterprise

Projects and custom instructions — testing instruction hierarchy, project-data boundary integrity, and artifact exfiltration.

AWS

Bedrock & Bedrock Agents

Testing Lambda tool boundaries, Knowledge Base poisoning, and cross-account IAM abuse via agent action groups.

LC

Custom LangChain / LlamaIndex

Testing chain-of-thought leakage, retrieval grounding bypass, and downstream tool injection in custom applications.

CLIN

Clinical-Domain LLMs

Abridge, Nuance DAX, Microsoft DAX Copilot — testing clinical accuracy drift under adversarial audio/transcription inputs and PHI handling.

FAQ

Frequently Asked Questions

Do we need an AI red team if we are only using Microsoft Copilot and not building custom AI?

Yes, if you have enabled Copilot Graph connectors or Copilot Studio agents. The moment Copilot touches SharePoint, OneDrive, Exchange, Teams, or Dataverse content with PHI, PCI, or CUI, your tenant-specific configuration determines your actual risk exposure, and the Microsoft-side red-team work does not cover your data, your permission model, or your user behavior. We have found cross-site permission leakage, ingestion of stale confidential documents, and cross-user prompt bleeding in well-configured Microsoft 365 tenants. If you are only using basic Copilot chat without data connectors, the risk is lower but non-zero.

How is an AI red team different from a traditional penetration test?

A traditional pen test targets network, application, and identity surfaces. An AI red team targets model behavior, prompt boundaries, training data, RAG indexing, and agent tool permissions. The methodologies are related but the attack catalogs are almost entirely different. Organizations with mature pen-test programs still need AI red-teaming as a complementary capability. We often recommend running both annually, scheduled six months apart.

Will red-team testing damage our production AI system?

No. Our engagement rules of engagement explicitly prohibit destructive testing in production. We test read-only exfiltration patterns, prompt boundary violations, and configuration flaws. For agentic deployments with write access, we test in a staging or dedicated test tenant. If the client only has a production environment, we define test accounts with scoped access and coordinate every test with the client team in a shared channel.

How does this map to NIST AI RMF or the EU AI Act?

Every finding is tagged with the relevant NIST AI RMF function (govern, map, measure, manage) and subfunction, plus OWASP LLM Top 10 2025 category, plus MITRE ATLAS technique where applicable. For EU AI Act conformity assessment, the red-team report supports the risk management, technical documentation, and post-market monitoring requirements for high-risk AI systems. For US regulatory contexts, we map to the HIPAA Security Rule, SEC Rule 10b5-1, FINRA 2210, CMMC 2.0 SC and SI families, and applicable state AI governance laws (California SB 1047 successor, Colorado AI Act, Utah AI policy).

Can this satisfy our cyber insurance AI governance questions?

Yes. Cyber insurance underwriters in the 2025 and 2026 renewal cycles are asking for evidence of AI inventory, AI governance, and AI security testing. Our red-team report, combined with VERITY compliance advisory documentation and SENTRY’s ongoing AI observability monitoring, typically satisfies the underwriter questionnaire and has produced premium reductions of 8-18% for clients who complete the full Armorstack AI security package.

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