Threat models
Injection, exfiltration, tool abuse and poisoning — mapped to your architecture.
Popular Resource
Enterprise AI Security Guide
A security-first guide covering threats, controls, and operating models for production AI.
- 7AI-specific threat classes covered
- 3control layers with implementations
- 1incident-response runbook included
Inside the guide
What you'll learn to defend
Guardrails, risk and compliance for enterprise AI systems.
Control layers
Input screening, output filtering and action gates with implementation patterns.
Incident response
Detection, containment and forensics for AI-specific incidents.
Vendor risk
Assessing third-party AI tools before they touch enterprise data.
What's inside
Security for systems that talk back
AI systems accept adversarial input as a feature and can act on your systems. That combination needs controls traditional appsec doesn't provide.
Threats
The attack surface, mapped honestly
Prompt injection chains, retrieval poisoning, tool-call escalation, data exfiltration through outputs — each explained with realistic scenarios against enterprise architectures.
- Seven threat classes with worked examples
- Attack trees for common architectures
- Which threats matter for which system types
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Defenses
Controls that don't live in the prompt
Layered enforcement outside the model: input screening, output filtering, argument-validated tool gates and least-privilege agent identities — with latency budgets and failure modes covered.
- Reference control architecture
- Implementation patterns per layer
- Red-teaming methodology to verify
Shared stateHandoffsRetriesTraces
Operations
When something gets through
AI incident-response runbooks: detection signals, containment moves that don't destroy evidence, and the postmortem-to-regression-test loop that makes each incident the last of its kind.
- Detection signal catalog
- Containment and forensics runbook
- Findings-to-eval regression loop
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Who it's for
For the people signing off
CISOs
The briefing that makes AI risk concrete.
Security architects
Control patterns ready for design reviews.
AI engineers
What security will ask, answered in advance.
Risk teams
Framework mapping to existing control catalogs.
Procurement
Vendor AI assessment checklists.
Incident responders
The runbook for a new incident class.
Delivery
Learn, then apply
Practical guides and tools from teams who ship production AI — so your next decision is grounded in real delivery experience.
- Actionable playbooks
- Architecture blueprints
- Cost & ROI frameworks
- Security checklists
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1
Assess
Understand readiness, risks and opportunities.
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2
Learn
Use playbooks, guides and architecture patterns.
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3
Decide
Prioritize the right build path.
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4
Execute
Partner with Skugal to ship confidently.
FAQ
Common questions
Is a system prompt a security control?
No — it's a suggestion to a probabilistic system. The guide's core argument: enforcement must live outside the model, and it shows how.
Does it map to frameworks like SOC 2 or the EU AI Act?
Yes — control mappings to common frameworks are included, so the work slots into your existing compliance program.
Is it too technical for leadership?
Chapter one is written for executives; the rest for the teams implementing. Share accordingly.
Next step
Get the security guide
Read it before your red team — or an attacker — writes the summary for you.