Three boundaries, one route that actually connects. It is validated, not inferred from configuration.
What is AI asset discovery?
AI asset discovery is the inventory of every model, agent, API and data pipeline in use, including tools staff adopted without a security review. Panop puts those AI assets into the same exposure model as cloud and application systems, then traces which of them can reach regulated or confidential data.
Why is AI security posture hard to manage?
AI systems are deployed faster than security teams can inventory them, and the tooling that governs cloud and application risk was not built to reason about models, agents or the data they can reach.
01
Limited AI Visibility
Organisations often lack a complete inventory of AI models, agents, APIs, and shadow AI, making it difficult to understand and manage their expanding AI attack surface.
02
Emerging AI Threats
AI systems introduce new risks such as prompt injection, data leakage, model manipulation, and excessive agent permissions that traditional security tools cannot effectively detect or validate.
03
Fragmented Risk Management
AI security is frequently disconnected from existing cybersecurity programs, making it difficult to prioritise AI risks alongside cloud, identity, and application exposures while meeting evolving governance and compliance requirements.
How does Panop secure AI models, agents and pipelines?
Panop brings AI assets into the same exposure model as the rest of your estate, so AI risk is prioritised alongside cloud, identity and application exposure.
01
Inventory every AI asset
Discover the models, agents, APIs and data pipelines in use across the organisation, including those deployed without security review.
+326/06 to 21/08
Critical27High203Medium259Low99
02
Surface shadow AI
Identify AI services reached by your applications and staff that never entered a governance process.
Business Risk Register
likelihood × impact, traced to the exposure driving it
ID
Business Risk
Band
L
I
Top Driver
Owner
Treatment
BR-1
Data Breach & Confidentiality
High
5
3
→Unrestricted file upload on portal
CISO
Mitigate
BR-2
Operational Disruption
High
5
3
→Unrestricted file upload on portal
CTO
Mitigate
BR-3
Fraud & Financial Loss
High
5
3
→Unrestricted file upload on portal
CFO
Accept
BR-4
Regulatory & Legal Exposure
High
5
3
→Deprecated TLS 1.0 on gateway
CLO
Mitigate
BR-5
Reputational & Brand Damage
High
5
3
→Deprecated TLS 1.0 on gateway
CMO
Mitigate
BR-6
Intellectual Property Theft
High
5
3
→Unrestricted file upload on portal
CISO
Mitigate
Every band traces back to a specific, validated exposure, so the treatment decision is defensible.
03
Map paths to sensitive data
Trace which models and agents can reach regulated or confidential data, and through which permissions.
Risk Evolution
exploitable chains, critical chokepoints & assets at risk over 30 days
Exploitable chainsCritical chokepointsAssets at risk
Daily snapshots accrue over time, and the trend fills in as history is recorded.
04
Govern against emerging requirements
Align AI exposure findings with the controls the EU AI Act and internal governance policies expect you to evidence.
Maturity Model
NIST CSF 2.0 · current profile against target
CurrentTarget profile
Every function is scored from live evidence, not a questionnaire, so the gap to target moves as the estate does.
What changes once AI assets are in the exposure model?
A complete inventory of AI assets and the data they can reach
Models, agents and AI APIs brought into scope alongside cloud and application assets
Shadow AI deployments surfaced before they reach production data
Excessive agent permissions identified through reachable data paths
AI risk prioritised in the same queue as the rest of the estate
Faster coordination between security, data and engineering teams on AI governance
With Panop, know which AI systems exist, what data they can reach, and which of those paths matter first.