Two ways to deploy the AI security platform.
One platform, the same four products in both tiers. Choose based on which AI workspaces you need to secure: web AI tools only, or local desktop agents and MCP-connected tools too.
Best Value
MagicMirror in
Browser
Browser-Native AI Security.
On-device protection for every web AI tool your employees use.
Best for
Organizations starting their AI security program. Browser-native orgs. Regulated industries needing a fast solve for web AI exposure.
Best for
- Browser extension across Chrome, Edge, Brave, Arc
- All four products: Risk Monitoring, Policy Enforcement, Data Protection (Marv), Insights
- On-device Small Language Models
- Coverage of 300+ web AI tools
Engagement
Discovery, pilot to a single team, phased rollout via your IdP or MDM.
Best for full coverage
MagicMirror for
Agents
Local-First AI Security.
On-device runtime control across every AI workspace your employees use.
Best for
Organizations going all-in on AI. Teams using desktop AI agents, IDE assistants, MCP-connected tools, or with clipboard-driven workflows.
Best for
- Everything in MagicMirror in Browser
- Local AI agents: Cursor, Claude Desktop, ChatGPT Desktop, native apps
- MCP tool call protection (bidirectional agent traffic)
- Clipboard movement detection
- On-device daemon coordinating every workspace
Engagement
Architecture review, pilot to a single team, phased rollout coordinated with your endpoint engineering team.
What the platform does
AI usage control, access control, and attack protection in one platform.
MagicMirror secures GenAI in the browser and on the endpoint, in real time. Detect AI-native attacks and enforce policy on-device without workflow friction.
AI Usage Control
See and control how AI gets used
Track prompts, uploads, and usage patterns at the browser level across every GenAI tool your teams touch. Turn invisible AI activity into policy you can actually enforce.
90% of AI logins are invisible to security teams today.
90% of AI logins are invisible to security teams today.
AI Access Control
Policies that adapt to user and context
Set access controls by identity and context: enforce stricter rules for personal logins, ease them for authenticated corporate accounts, and adjust file-upload scanning based on who's working and where.
70% of GenAI access runs through personal accounts.
70% of GenAI access runs through personal accounts.
AI Cybersecurity
Stop sensitive data before it leaves the device
On-device Small Language Models detect and anonymize PII, PCI, and PHI in real time, before a prompt reaches ChatGPT, Claude, or any external model. AI-layer security without sending your data to the cloud to protect it.
40% of files uploaded to AI tools contain PII or PCI.
40% of files uploaded to AI tools contain PII or PCI.
AI Risk Management
Board-ready visibility into AI risk and ROI
Quantify exposure and value across every AI tool in one dashboard. Give leadership a single source of truth for governance and compliance, and the evidence to decide where AI spend earns its keep.
71% of CIOs say AI usage doesn't match spend.
71% of CIOs say AI usage doesn't match spend.
Trusted by
Trusted by security teams who don't have time to guess.
Customers & Partners
Common questions
AI Security FAQs
What should an enterprise AI security platform provide?
An effective Gen AI security solution should show where AI is used, assess risk in real time, and enforce policy across browsers, agents, and connected tools. Among AI security tools, AI leaders should prioritize actionable context, on-device controls, and coverage that does not disrupt approved workflows.
How does prompt injection detection identify AI threats?
Prompt injection detection examines prompts, retrieved content, model responses, and tool activity for instructions that could alter intended behavior. A prompt injection attack may be entered directly or hidden within external content, so effective AI prompt injection controls must evaluate context rather than rely only on keyword matching.
How can organizations prevent prompt injection attacks?
Prompt injection prevention requires layered safeguards, including content inspection, restricted permissions, output validation, and approval gates for sensitive actions. Effective prompt injection protection reduces exposure without blocking legitimate use. For prompt injection, AI controls should consider the user, data, connected tool, and requested action.
What should protection against an AI prompt injection attack cover?
Protection should extend beyond typed prompts to browser content, documents, emails, agent instructions, model responses, and downstream tool actions. Because malicious instructions can enter through several sources, controls should evaluate both the content presented to the model and the action the system is being asked to perform.
What should MCP tool poisoning detection monitor?
MCP tool poisoning can hide malicious instructions in tool descriptions, parameter schemas, or return values. Detection should inspect tool definitions, flag post-approval changes, validate outputs, and monitor permissions, tool calls, and sensitive downstream actions.
How can enterprises reduce tool poisoning attacks?
Controls for a tool poisoning attack should include trusted-server allowlists, schema validation, least-privilege permissions, change monitoring, and user confirmation for sensitive actions. Reducing MCP tool poisoning attacks also requires runtime visibility because a previously approved tool or server may change after its initial review.
How does MagicMirror strengthen enterprise AI security?
MagicMirror protects AI use across browser tools and agentic endpoints. It detects prompt injection across both surfaces, helps block MCP tool poisoning, helps stop supply-chain attacks at MCP launch, and enforces policy on-device before risky actions are executed.
