Digital Defense helps enterprises safely deploy Claude, ChatGPT, Copilot, Gemini and AI agents by identifying AI usage risks, designing governance controls, deploying DLP / CASB / SSE / AI gateway platforms, and integrating AI activity monitoring with SOC and compliance workflows.
AI platforms & surfaces we help secure
Your existing CASB, DLP and email security are still essential — they are simply not designed for prompts, embeddings, agent connectors and autonomous actions. These are the attack surfaces that legacy controls do not see.
Untrusted content hijacks agent behavior — the most prevalent attack class for agentic AI.
Reworded, encoded, or semantically inferred sensitive content slips past keyword/regex DLP.
Agents send, delete, sign, or modify data without runtime approval gates.
MCP / API connector tokens receive excessive permissions, expanding the blast radius.
Employees use personal ChatGPT / Claude / Gemini outside any enterprise control.
Ollama, LM Studio and other on-device models bypass DLP, CASB, and SOC monitoring entirely.
Developers paste proprietary code into Claude Code or Copilot; secrets and IP leak.
AI side-panel extensions exfiltrate page DOMs into third-party models.
Adversarial content in training corpora or RAG sources corrupts model outputs.
Sensitive documents recoverable through embeddings, similarity search, or weak access control.
Fireflies, Otter, Gong-style bots capture confidential discussions into third-party clouds.
Privileged users harvest data at scale through legitimate AI queries.
Ungrounded outputs flow into customer-facing or decision-supporting workflows.
No conversation history, no evidence trail, no incident reconstruction.
Digital Defense assesses AI adoption risk through a structured risk register covering governance, identity, DLP, agent autonomy, prompt security, RAG security, AI coding assistants, browser extensions, SaaS AI usage, vendor risk, audit retention and SOC monitoring.
Risk examples
Recommended controls
Business outcome
Board-ready evidence that AI adoption is governed, accountable, and defensible.
Risk examples
Recommended controls
Business outcome
Sensitive data stops leaving the perimeter through AI channels — without blocking adoption.
Risk examples
Recommended controls
Business outcome
Agents are useful but cannot cause catastrophic outcomes autonomously.
Risk examples
Recommended controls
Business outcome
Developer velocity is preserved while IP and secrets stay inside the boundary.
Risk examples
Recommended controls
Business outcome
Visibility on every AI tool in use, and a sanctioned channel users actually prefer.
Risk examples
Recommended controls
Business outcome
AI incidents are detectable, investigable, and explainable to regulators and the board.
Each row links a concrete AI risk to the right control class, the platforms that deliver it, and the Digital Defense service that operationalizes it.
| Risk Area | Example Risk | Recommended Control | Technology Examples | Digital Defense Service |
|---|---|---|---|---|
| Shadow AI usage | Employees using personal ChatGPT / Claude / Gemini accounts | CASB, SSE, SWG, AI app discovery | Netskope, Zscaler, Defender for Cloud Apps, Harmonic Security, Nudge Security | Shadow AI Discovery Assessment |
| Confidential data upload | Sensitive files uploaded to public AI tools | Semantic DLP, endpoint DLP, user coaching | Cyberhaven, Microsoft Purview, Nightfall AI, Harmonic Security | AI DLP Architecture & Deployment |
| Prompt injection | Malicious content hijacking AI agent behavior | AI gateway, prompt security, red teaming | Lakera Guard, Prompt Security, Pillar Security, Microsoft PyRIT | Prompt Injection Risk Assessment |
| Agent autonomous actions | AI agent sending / deleting / signing without approval | Human-in-the-loop, conditional access, approval workflow | WitnessAI, Entra ID, Logic Apps, Power Automate | Agentic AI Governance Design |
| MCP / plugin risk | Over-privileged MCP connector tokens | OAuth governance, least privilege, token review | Entra ID Governance, Astrix Security, Harmonic Security | MCP / Connector Security Review |
| AI code exfiltration | Developers pasting code into Claude Code / Copilot | Secret scanning, code DLP, endpoint monitoring | Cyberhaven, GitGuardian, GitHub Advanced Security, Defender for Endpoint | AI Coding Assistant Security Review |
| Local / offline AI models | Ollama or LM Studio bypassing enterprise controls | Endpoint control, app allowlisting, EDR | Defender for Endpoint, App Control for Business, Cyberhaven | Local AI Control Assessment |
| Meeting transcription risk | Fireflies / Otter / Gong capturing confidential calls | CASB block / allow, approved-tool policy, DLP | Defender for Cloud Apps, Netskope, Zscaler, Nightfall AI | AI Meeting Tool Governance |
| RAG / vector DB leakage | Sensitive docs exposed through embeddings or vector search | DSPM, CSPM, RAG security, access control | Protect AI, HiddenLayer, Wiz, Defender for Cloud | RAG Security Architecture Review |
| Audit & forensic gaps | No conversation history or evidence trail for AI incidents | SIEM integration, audit retention, incident playbooks | Microsoft Sentinel, Splunk, Datadog, AI gateway logs | AI SOC Monitoring & Forensics |
Next step
Build your AI governance roadmap on a real risk register.
We are vendor-aware, not vendor-locked. Digital Defense is a consulting, assessment, architecture and operationalization partner — we help you choose what fits, deploy it correctly, and run it well.
Whether you are rolling out Claude Chat, Claude Code, Claude Cowork, Claude Enterprise, MCP connectors, source-code integration, internal knowledge bases or agentic workflows — there is a known, structured set of readiness gates. We assess each one and tell you exactly where you stand.
11-point readiness checklist
A phased rollout — from scope and governance through identity, data, runtime, SOC, training, pilot and continuous improvement. Each phase has clear inputs, deliverables and outcomes.
Define Anthropic / OpenAI / vendor enterprise plan, DPAs, allowed and disallowed v1 use cases, named owners.
Customer inputs
Stakeholder workshops, vendor contracts, target use cases
Deliverable
Scope document, RACI, signed DPAs
Outcome
Everyone agrees what v1 includes and excludes.
AI acceptable use policy, AI risk intake, NIST AI RMF / ISO 42001 alignment, refreshed data classification.
Customer inputs
Existing policies, regulatory profile, board priorities
Deliverable
AI policy pack, risk register, compliance map
Outcome
AI adoption is governed, not improvised.
SSO + SCIM, MFA, Conditional Access with device posture, least-privilege OAuth on every MCP connector, plugin allowlist.
Customer inputs
IdP access, connector inventory, device posture data
Deliverable
Identity & connector hardening runbook
Outcome
Non-human identities are scoped, named, and revocable.
AI-aware DLP at egress, endpoint controls for local AI, secret scanning on agent-written code, egress allowlist, default computer-use disabled.
Customer inputs
Data classification, sensitive corpora map, DLP licences
Deliverable
DLP policy matrix, code DLP rules, egress allowlist
Outcome
Sensitive data stops at the perimeter — silently and reliably.
AI gateway with prompt security, sandbox hardening, plugin signature verification, browser-automation gating.
Customer inputs
Agent inventory, runtime architecture
Deliverable
AI gateway architecture, runtime control catalog
Outcome
Untrusted content cannot weaponize your agents.
Centralize Claude / connector / AI gateway logs into Sentinel or Splunk, baseline anomaly rules, leadership dashboards.
Customer inputs
SIEM access, audit logs, KPI definitions
Deliverable
AI detection use cases, dashboards, response playbooks
Outcome
AI incidents are detectable, investigable, and reportable.
Mandatory user training, departmental champions, frictionless sanctioned path so the safe path stays the easy path.
Customer inputs
Org chart, change-management partner
Deliverable
Training pack, champions network, comms plan
Outcome
Users prefer the sanctioned channel over shadow AI.
Scoped department pilot with success criteria, tabletop incident scenarios, expansion by use case (not by user).
Customer inputs
Pilot team, success criteria, escalation paths
Deliverable
Pilot report, tabletop outcomes, expansion roadmap
Outcome
Adoption scales with evidence, not optimism.
Quarterly connector & plugin reviews, annual third-party AI red teaming, vendor product-update monitoring.
Customer inputs
Pilot data, vendor advisories, threat intel
Deliverable
Quarterly governance report, red team findings, action log
Outcome
AI security posture improves continuously, not retroactively.
How we operate
Five principles that shape every Digital Defense engagement — calibrated to keep AI useful while keeping irreversible outcomes under human control.
Every MCP connector and plugin starts in read-only mode. Write, send, sign and delete are unlocked per use case after an explicit risk review — never by default.
Agents may propose; humans approve. The single highest-leverage control — minimal productivity cost, maximum catastrophic-outcome reduction.
Treat every AI agent and connector as a service account: explicit scopes, time-bound access, named owner, quarterly review.
Web pages, MCP responses, documents and connector data are all potential prompt-injection payloads. Build controls around that assumption.
Shadow AI is a UX problem. When the sanctioned tool is faster and better than the personal account, employees adopt it without coercion.
Three engagement paths — start with an assessment, implement controls, or hand over the run.
Start here. 2–4 weeks.
Scope
Deliverables
Build the controls. 8–14 weeks.
Scope
Deliverables
Run it for you. Ongoing.
Scope
Deliverables
Whether your teams are already using Claude, ChatGPT, Copilot and Gemini, or you are about to license Claude Enterprise — we will help you discover, govern, deploy, and monitor.
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