Adopt AI with confidence — not blind spots.
Every model, prompt, agent and MCP server your teams adopt is a new way in. Understand the AI attack surface, assess where you stand, and build AI systems you can put in front of real users. Start with the pillars below — and a self-assessment you can run today.
The challenge
Your teams aren’t waiting for security to approve AI
AI adoption is bottom-up and fast. The risk isn’t one big system — it’s the long tail of tools, prompts and agents that never went through review.
Shadow AI
Teams adopt AI tools and browser extensions faster than security can review them, so sensitive work flows through apps no one has vetted.
Data in prompts
Proprietary code, customer records and strategy end up pasted into third-party models — often with no record of what left the building.
Over-permissioned agents
Agents and MCP servers are frequently granted far broader access than the task needs, turning one compromised tool into many exposed systems.
The landscape
The pillars of AI security
Securing AI end to end — from build time to runtime — spans eight areas. Use them as a checklist for your own programme; each is a place a gap can turn into a breach.
AI visibility & inventory
Know every model, agent, MCP server and AI-enabled SaaS in use — with an owner and a purpose for each.
Shadow AI discovery
Surface the unsanctioned tools and extensions employees adopt on their own, before data leaves through them.
Prompt & data protection
Keep proprietary and personal data out of third-party models; inspect and control what gets sent, and to whom.
Model & output safety
Defend against prompt injection and jailbreaks, and stop unsafe or hallucinated output from driving real actions.
Agent & MCP security
Treat tools as a trust boundary: verify tool definitions, scope permissions, and gate high-risk agent actions.
Identity & least privilege
Give agents first-class, scoped identities and continuously right-size their access to the minimum they need.
Runtime monitoring & guardrails
Watch live behaviour, alert on anomalies, and constrain or block actions that break policy in the moment.
Governance & compliance
Set policy, keep an audit trail, and align to evolving AI regulation and standards so you can prove control.
How we help
Where LyboAI helps you today
LyboAI focuses on the parts of AI security you own before anything reaches production: assessing your posture, upskilling your people, and building AI and MCP systems securely. For live runtime controls, we help you scope requirements and choose the right platform.
MCP Security Checker
Scope a weighted self-assessment to what you're building and get a graded posture with a worst-first fix list.
Run the checkerOn-Device AI Starter & Security Checklist
Starting mobile or Angular development with on-device models? Runtime options, four delivery models, and a tailored security and coding checklist.
Open the checklistBuilding Secure MCP Servers
The Academy course: architecture, safe tool design, prompt-injection controls, auth and hardened deployment — with diagrams.
Open the courseVALUE-AI framework
Assess an AI initiative across five pillars before you build — so security and governance are decided up front, not bolted on.
Explore the frameworkAI Academy
Self-paced courses that turn AI security from a specialist topic into a skill your whole team shares.
Browse coursesBuilt for the compliance era
AI regulation is arriving through existing law — consumer-protection rules on AI claims, privacy duties on automated decisions, and sector guidance. Securing and governing your AI now is what lets you keep saying yes to it later.
LyboAI’s own security material, aligned to established guidance — OWASP GenAI, SlowMist, Palo Alto Networks. Guidance reduces risk; it is not a certification or a substitute for testing and legal review.