Solutions
Built for the enterprises where an uncontrolled agent costs more than the platform
The initial market should favor organizations that already have meaningful AI-agent adoption, sensitive systems, mature security teams and a reason to prove control. Start where the cost of an uncontrolled agent is materially higher than the subscription price and where integration sophistication is an advantage rather than a barrier.
Use cases
Five things a customer can prove in the first deployment
In the interactive demo you pick an agent, see its effective authority, apply a policy and watch the decision it produces.
Discover shadow agents and orphaned service identities in cloud, source code and AI platforms.
Block unauthorized data exports or dangerous tool calls at runtime.
Require human approval for high-impact actions such as payments, deletion, privilege changes or bulk external messaging.
Record every agent/tool transaction with actor, delegation chain, policy result and evidence.
Attribute model/tool consumption to agent, team, application and business purpose.
MVP DEMO
Agent attempts a risky action → Agent Trust Cloud explains the risk → policy blocks or requests approval → evidence is recorded.
Buying committee
Buyer Personas & Buying Committee
Agent Trust Cloud will rarely be purchased by one person. Security owns the risk, platform engineering owns deployment, AI teams own agent velocity, audit owns evidence and finance cares about spend. The product and sales process must give each stakeholder a reason to support the same platform.
CISO
Reduce uncontrolled autonomous access and prove governance.
CIO / CTO
Deploy agents faster without creating fragmented control systems.
IAM / PAM leader
Represent nonhuman identities and delegated authority.
AI platform leader
Standardize policy and observability across model and framework choices.
Internal audit / GRC
Generate evidence and control mappings continuously.
CFO / FinOps
Attribute and constrain AI, model and tool spend.
BUYING LOGIC
The strongest deal has one risk owner, one deployment owner, one AI champion and one executive sponsor.
Industry buying paths
One control plane, evidence shaped for each operating environment
The product capabilities stay the same. The accountable owner, protected systems, approval boundary and evidence package change with the industry and platform.
AI agent security for financial services
Financial services and insurance
Govern bank and insurer agents that can move money, change customer records or influence regulated decisions. Evidence supports AI governance for banks, DORA AI compliance work and insurance oversight without claiming that software replaces a regulated entity’s legal assessment.
AI compliance for healthcare
Healthcare and pharma
Apply identity, least privilege, data controls and action evidence to clinical and operational agents. HIPAA AI compliance, AI governance for healthcare and pharma validation still require the organization’s privacy, quality and legal owners.
AI governance for the public sector
Public sector
Keep ownership, authorization, regional processing and evidence explicit for government workloads. FedRAMP AI readiness is an evidence and boundary question—not a certification the platform can award itself.
AI agent security for SaaS, ERP, CRM and ITSM
SaaS and enterprise platforms
Put the same control boundary around embedded agents in SaaS products and agents connected to ERP, CRM and ITSM systems, where a tool call can become an approved business transaction.
Copilot governance and security
Microsoft Copilot estates
Bring Copilot governance, Copilot security and Copilot data security into the same inventory, owner, permission and evidence model used for custom agents.
Market entry
Ideal Customer Profile & Market Entry
Priority sectors
- Financial services
- Software & cloud
- Manufacturing
- Energy
- Insurance
- Business services
- Regulated enterprises
Trigger events
- Agent rollout
- Internal AI platform launch
- MCP adoption
- AI governance program
- Audit finding
- Security incident
- Rapid AI cost growth
How we deploy
- Create an account scoring model using employee count, AI hiring, cloud footprint, security maturity, regulated-data exposure and agent-program signals.
- Sell the first proof of value into one controlled environment rather than asking to secure the whole company on day one.
- Target a 2-4 week discovery/governance proof before enforcing high-risk runtime policies.
- Create deployment options for SaaS control plane with customer-managed gateway/data plane where required.
What a good pilot proves
- ICP definition is encoded in CRM and outbound targeting.
- Pilot scope can be deployed without a multi-quarter professional-services project.
- Reference architecture exists for cloud-first and high-control enterprises.
ENTRY WEDGE
Lead with shadow-agent discovery + high-risk permission visibility, then activate enforcement.