AI SECURITY & GOVERNANCE
Adopt AI without losing control of your data, risk, or security.
Generative AI, enterprise copilots, and autonomous agents can transform operations—but they also introduce new challenges around sensitive data, access, third-party services, and automated actions. InfoSec LI helps small and midsize organizations implement practical security architectures and governance guardrails while keeping the business moving.
CONTROLLED AI / TRUST ARCHITECTURE
User / Application
Verified identity and delegated permissions
AI Agent
Scoped actions and human approval
Authorization Boundary
Token boundaries, least privilege, transaction limits
MCP / API Tool
Authenticated tool execution and logging
Enterprise Resource
Explicit access checks and audit trails
High-risk actions require appropriate human approval; tool access does not imply unrestricted resource access.
Identity-led access. Explicit action boundaries. Auditable decisions.
THE AI RISK REALITY
Innovation without visibility creates unmanaged risk.
AI adoption often moves faster than governance, data controls, and security architecture. Organizations need visibility into how AI is being used, what information it can reach, and what actions it is allowed to take.
Silent Data Exposure
Employees may enter proprietary code, customer information, internal documents, credentials, or business data into public or unapproved AI platforms.
Overshared Enterprise Data
Microsoft 365 Copilot generally respects the permissions users already have. It can make legacy oversharing and poorly governed SharePoint, OneDrive, Teams, and Microsoft 365 content access more visible—not create those permissions.
Autonomous Agents & Integrations
AI agents and automated workflows may interact with APIs, tools, data sources, service identities, secrets, and business systems without appropriate privilege boundaries, logging, or oversight.
AI SECURITY CAPABILITIES
Four disciplines for secure AI adoption.
InfoSec LI brings governance, engineering, data protection, and operational security together so AI can be adopted without creating uncontrolled exposure.
01 /
AI Governance, Readiness & Third-Party Risk
Establish operational oversight, acceptable use, ownership, and vendor accountability.
02 /
Workplace AI, Copilot & Data Protection
Keep sensitive corporate, customer, employee, and regulated information within controlled boundaries.
03 /
AI Application, Agent & Integration Security
Apply traditional security engineering principles to AI applications, autonomous agents, and interconnected systems.
04 /
Human Defense & Incident Readiness
Prepare people and response processes for both AI misuse and AI-enabled attacks.
TRUST BOUNDARIES / CONTROLLED DATA FLOWS
Control what AI can see—and what it can do.
AI Data Boundary
User Prompt
Prompt and file context
Policy / DLP Controls
Evaluate sensitive data where controls support it
Enterprise AI Boundary
Approved service, access and retention policies
Model
Authorized processing within agreed boundaries
Evaluate data before it crosses a trust boundary. Inspection coverage varies by platform and deployment.
Enterprise AI Data Access
User Identity
Authenticate the requesting user
Enterprise AI
Preserve user authorization context
RAG Layer
Apply identity-based retrieval controls
Authorized Data Sources
Enforce source permissions and least privilege
Retrieval should inherit or enforce the requesting user’s authorization—not broaden it.
BUILT AROUND RECOGNIZED AI RISK AND SECURITY PRACTICES
Pragmatic controls mapped to recognized industry guidance.
InfoSec LI focuses on practical security outcomes rather than compliance theater. AI assessments, governance programs, and architectures can be informed by and mapped to recognized frameworks and industry guidance.
NIST AI Risk Management Framework 1.0
Govern, Map, Measure, and Manage: a practical structure for AI risk ownership, context, measurement, and response.
NIST AI 600-1
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. A companion resource for managing risks specific to generative AI.
OWASP Top 10 for LLM Applications 2025 / GenAI Security Guidance
Controls mapping informed by Prompt Injection, Sensitive Information Disclosure, Supply Chain, Improper Output Handling, Excessive Agency, Vector and Embedding Weaknesses, and Misinformation. No OWASP certification is implied.
ISO/IEC 42001:2023
An AI management system standard that can help organizations establish, implement, maintain, and improve AI governance. Engagements may support alignment, readiness, and controls mapping—not certification services.
Engagements focus on defensible security posture, architecture readiness, governance maturity, and controls mapping. Alignment and readiness work do not constitute certification.
WAYS TO ENGAGE
Choose the level of AI security support your organization needs.
AI Readiness Assessment
CORE FOCUS
Existing organizational AI usage, Shadow AI, governance, data exposure, technical risk, and executive visibility.
PRIMARY DELIVERABLE
Current-state AI security assessment plus a prioritized 90-day remediation roadmap.
BEST FOR
Organizations beginning their AI journey or leadership teams that need visibility into existing exposure.
M365 Copilot Security Readiness
CORE FOCUS
Microsoft 365 permissions, SharePoint and OneDrive oversharing, sensitivity labels, Purview/data controls, identity, and deployment guardrails.
PRIMARY DELIVERABLE
Copilot security-readiness report, prioritized remediation actions, and secure rollout recommendations.
BEST FOR
Organizations planning, piloting, or pausing a Microsoft 365 Copilot deployment.
AI Governance & Policy Package
CORE FOCUS
Organizational AI rules, approved platforms, risk classification, inventory, use-case intake, accountability, and workforce education.
PRIMARY DELIVERABLE
Tailored AI acceptable-use policy, AI inventory framework, governance workflow, RACI, and employee-awareness package.
BEST FOR
Leadership teams that need immediate organizational guardrails without building a large AI governance function.
AI Application & Agent Review
CORE FOCUS
AI applications, autonomous agents, prompt injection, MCP, APIs, secrets, authorization, tool access, and excessive agency.
PRIMARY DELIVERABLE
Technical architecture and security review with identified weaknesses, attack scenarios, and prioritized remediation recommendations.
BEST FOR
Engineering or IT teams building, deploying, or integrating custom internal AI applications and agents.
AI Security Architecture Review
CORE FOCUS
Enterprise AI data flows, identity boundaries, DLP, RAG, model/tool integrations, network architecture, logging, and data access.
PRIMARY DELIVERABLE
Target-state AI security architecture with trust boundaries, control recommendations, and implementation priorities.
BEST FOR
Mid-market IT and security teams integrating AI into production business environments.
COMMON SCENARIO · ILLUSTRATIVE
Preparing Microsoft 365 for Copilot
An organization plans to introduce Microsoft 365 Copilot but discovers years of broad SharePoint and OneDrive access. Before rollout, the priority becomes identifying overshared content, tightening permissions, applying appropriate data controls, and establishing governance guardrails.
Illustrative scenario—not a customer case study or a claim of a completed InfoSec LI engagement.
TAKE CONTROL
AI is already entering your business. The question is whether it is happening with the right guardrails.
Schedule a direct, vendor-neutral conversation to understand your current AI exposure and build a practical roadmap for secure adoption.
Practical security for a more resilient tomorrow.
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