Security validation for AI-enabled systems.
Most security frameworks weren't designed for LLM applications, RAG pipelines, or AI agents. SCS provides structured review of the attack surfaces that come with AI adoption — before adversaries find them.
Organizations deploying AI in production environments.
AI security is not a theoretical concern. As AI-powered features ship into production, the attack surface expands in ways that traditional testing frameworks were not built to evaluate.
If your team has shipped or is building LLM features, AI agents, or RAG systems — especially in environments handling sensitive data or regulated information — this assessment is for you.
- Engineering teams shipping LLM-powered features in production applications
- Organizations using AI agents with access to internal tools, APIs, or databases
- Security leaders responsible for AI systems in regulated or high-trust environments
- Companies building RAG systems over sensitive internal data
- Enterprises evaluating AI adoption risk before deployment
Automated scanning cannot evaluate AI system behavior.
Prompt injection paths, tool execution trust chains, and context boundary failures require human analysis of how the system actually behaves under adversarial conditions — not static code review alone.
The same reasoning that makes human security testers better than scanners for business logic applies here: understanding the system's intent, its assumptions, and where those assumptions break.
AI security for real deployments.
SCS does not sell generic AI strategy decks. We help organizations adopt and deploy AI safely by validating the systems, permissions, data paths, and controls that determine real-world risk.
Microsoft 365 Copilot Exposure Assessment
Many Copilot risks are not jailbreaks. They are governance failures made visible by AI: stale group memberships, overshared SharePoint sites, broad Teams membership, organization-wide links, connector permissions, and sensitivity labels that do not match the business risk. SCS validates whether those conditions become discoverable through Copilot using an internal evidence-first assessment framework.
What We Validate
- Can standard or limited users discover HR, legal, finance, audit, executive, or workforce-planning content?
- Do Copilot citations reveal sensitive file names, SharePoint locations, Teams, connectors, or metadata?
- Do seeded canaries appear to unauthorized roles?
- Do connector-backed sources expose more than expected?
- Do different roles receive appropriately different answers?
- Can access-path evidence, when inventory is provided, explain why a user had access?
What You Receive
- Copilot Exposure Summary
- Role Exposure Matrix
- Sensitive Category Findings
- Connector Exposure Findings
- Evidence provenance: output, citation, source path, connector, label, or canary
- Optional inventory and access-path root cause context
- Executive summary, technical report, findings CSV, redacted evidence bundle, and retest plan
Fixed-scope entry point: Copilot Exposure Snapshot
For teams that need a faster first transaction, SCS offers a Copilot Exposure Snapshot: 3-5 business days, starting at $3,500-$7,500. It covers 3-5 tested roles/personas, limited SharePoint/Teams/OneDrive scope, optional seeded canaries, a sample prompt pack, Role Exposure Matrix, executive summary, top 10 remediation actions, and retest checklist. The full Microsoft 365 Copilot Exposure Assessment runs 1-3 weeks and typically starts at $12,500-$35,000 depending on tenant complexity, roles, connectors, evidence requirements, and remediation workshop needs.
Evidence-first, not prompt-list testing.
SCS does not treat Copilot assessment as a generic jailbreak exercise. We test as defined identities, compare role outcomes, capture citations and sources, use seeded canaries where appropriate, and distinguish confirmed exposure from metadata exposure, likely oversharing, expected access, and inconclusive results.
Example finding
A standard finance persona asks Copilot about workforce reduction planning. Copilot cites a document in an Executive Planning SharePoint site. The report shows the identity, role, source path, evidence provenance, expected access, likely access path, remediation owner, and retest command.
Focused AI security assessment areas.
SCS scopes AI testing by architecture and evidence source. These focused pages explain how we evaluate common AI security problem areas without turning the engagement into a generic prompt list.
LLM Security Assessment
Prompt injection, system prompt exposure, policy bypass, output handling, and LLM data leakage.
RAG Security Assessment
Retrieval leakage, document poisoning, tenant isolation, source citations, and knowledge base access control.
AI Agent Security Assessment
Tool misuse, unsafe actions, workflow abuse, API trust chains, and approval boundaries.
Prompt Injection Testing
Direct and indirect prompt injection testing in the context of the deployed AI architecture.
AI Data Leakage Assessment
Data exposure through model output, retrieval context, citations, logs, tools, and connectors.
Copilot Permissions Audit
AI-discoverable exposure from stale groups, broad permissions, sharing links, labels, and connector scope.
SharePoint Copilot Exposure
SharePoint, Teams, OneDrive, and connector-backed sources surfaced through Copilot citations and answers.
Assessment Methodology
The internal evidence-first framework SCS uses to structure suites, identity matrices, findings, and reports.
What gets tested.
Every engagement is scoped to the specific AI system under review. The table below reflects the full assessment framework.
| Attack Surface | What We Evaluate |
|---|---|
| Prompt Injection | Direct user input injection; indirect injection via external content (web, files, email); system prompt extraction and override attempts |
| Insecure Tool Execution | AI agent calls to APIs, code execution environments, file systems, and databases; trust chain validation; privilege escalation via tool misuse |
| Data Leakage | PII and sensitive data exposure through model output; system prompt extraction; RAG context bleed between users or tenants |
| Authorization Boundary Failures | Identity confusion between user and AI system; privilege escalation via prompt manipulation; cross-tenant data access in shared AI deployments |
| Unsafe Output Handling | LLM output injected into downstream processes (HTML rendering, SQL queries, shell commands, code execution) without sanitization |
| Secrets & Credential Exposure | API keys in model context or tool configurations; secrets accessible through conversation history; environment variable exposure |
| Logging & Privacy Risks | Sensitive data persisted in model logs; audit gap analysis; PII exposure in conversation storage and analytics pipelines |
| Workflow Abuse Paths | Multi-step agent manipulation; chained tool abuse to achieve unintended outcomes; goal hijacking in autonomous AI workflows |
| RAG System Security | Document injection and poisoning attacks; retrieval manipulation; embedding model vulnerabilities; knowledge base access control |
| Microsoft 365 Copilot & Enterprise AI Exposure | Testing whether Copilot or internal copilots surface sensitive enterprise content through overshared permissions, stale groups, connector access, labels, citations, source paths, or seeded canaries |
Common failure modes in AI-enabled applications.
Indirect Prompt Injection
External content retrieved by the AI system contains injected instructions that alter its behavior — without the user or operator seeing the attack. Common in RAG systems and AI agents that process third-party content.
Excessive Tool Permissions
AI agents granted read/write access to databases, file systems, or external APIs without scope restriction. A single successful prompt injection can escalate into a full data exfiltration path.
Tenant Data Leakage in RAG
Multi-tenant RAG deployments with insufficient isolation allow one user's queries to surface another tenant's documents. A retrieval misconfiguration, not a model vulnerability.
Permission Sprawl Made Discoverable
Sensitive content may already be accessible through old groups, inherited SharePoint permissions, broad Teams membership, or organization-wide links. Copilot can turn that hidden access into direct answers and citations.
Sensitive Metadata and Citation Leakage
Even when content is not fully summarized, Copilot may reveal sensitive file names, source locations, Teams, SharePoint paths, connector names, or labels.
Connector Oversharing
Enterprise connectors can expose sensitive source-system data if connector ACLs, permission modes, or source mappings are broader than intended.
Stale Group and Shadow Access
Users may retain access from old projects, inherited AD groups, or temporary assignments. SCS can test representative personas or shadow accounts and document clone limitations.
Unsafe Output Rendering
LLM-generated content rendered directly into HTML without escaping enables stored XSS. Output written to SQL queries or shell commands without sanitization enables injection.
System Prompt Extraction
Applications with confidential system prompts (containing business logic, internal instructions, or API credentials) are routinely extractable through adversarial conversation manipulation.
Authorization Bypass via Context
AI systems that make authorization decisions based on user-supplied context can be manipulated into granting access to data or capabilities the user is not entitled to.
What you receive at the end of an engagement.
- Findings Report — Risk-rated vulnerabilities with proof-of-concept reproduction steps and business impact analysis
- Attack Narrative — How a realistic adversary could chain findings to achieve meaningful access or data exfiltration
- Remediation Guidance — Developer-oriented fixes with implementation-level specificity, not generic "validate input" recommendations
- Findings Workshop — Live walkthrough of critical findings with your engineering and security team
- Retest Validation — Optional: verify fixes prior to production deployment
How an AI security engagement is scoped.
Every AI system is different. Before scoping, we need to understand: what model or service you're using, what tools or APIs the AI has access to, what data flows through the system, and who the users are.
Typical engagement timelines for AI security assessments run 5–10 business days depending on system complexity and the number of distinct AI components in scope.
Outcomes leadership can act on.
At the end of the engagement, your team understands which users or persona tiers can discover sensitive content, which repositories or connectors are involved, what evidence supports each finding, and what to retest after remediation.
Decision-ready findings
- Who saw what
- Where it came from
- Whether access was expected
- Why access likely existed
- What to fix first
- How to retest
Common Copilot assessment questions.
Does this mean Copilot bypassed permissions?
No. Most Copilot exposure risk comes from content users can technically access but were not intended to discover. The assessment validates whether that access becomes AI-discoverable through answers, citations, source paths, connector evidence, labels, or seeded canaries.
Do you need admin access?
Not always. Manual evidence capture and targeted exports can support many assessments. Deeper root-cause analysis may require local inventory exports or admin-assisted permission data for cited resources.
Do you upload test data into our tenant?
No. Seeded canaries, when used, are placed externally by the client or operator in approved locations. The assessment framework checks whether Copilot surfaces those canaries; it does not inject canary documents into Copilot requests.
Is this a Microsoft 365 governance product?
No. SCS provides an operator-led security assessment. Where governance tools show configuration state, we validate what AI-enabled users can actually discover and provide evidence, root cause context, and retest guidance.