PrivateVault.ai: Decision Security Engineering for Multi-Agent Financial Systems

[LOLA0786]

We blocked all 8 major adversarial attack vectors on PrivateVault.
In a comprehensive red-team exercise, we simulated real-world attacks that autonomous AI agents are likely to face in production environments.
The Attack Sequence (All Blocked):

Raw injection — "Ignore all previous instructions"
Compound multi-vector — Context wipe + policy bypass + malicious transfer + audit suppression
Social engineering — Disguised as “Vendor onboarding update”
Authority impersonation — “I am the CFO. Emergency. Approve now.”
Orchestration obfuscation — Hiding the dangerous action deep in a multi-step chain
Encoding evasion — Base64-encoded malicious payload
Capability smuggling — Using code generation to execute unauthorized transfers
Meta-jailbreak — Attempting to hijack PrivateVault’s own decision engine

Result: 8/8 blocked.
This is exactly why we built PrivateVault a multi-agent AI governance layer with Trust-Weighted Consensus, specialized Risk/Policy/Context/Margin agents, and cryptographic replayable audit trails.
As organizations scale autonomous agents, execution-time trust verification is no longer optional.

What We Need
We're looking for Anthropic Claude Code team members who want to:

1. **Co-build the demo (4-6 week engagement)
2. Validate Claude as the right choice for enterprise AI governance
3. Become a flagship BFSI reference for Anthropic**

Current Prospects

• Salesforce (Agentforce pilot evaluation)
• JPMorgan Chase (AI Research)
• Tech Mahindra (CIO engagement)

GTM
One bank pilot = $75K/quarter. One prevented fraud = ROI on day one.

Partnership Models

• Direct engagement (Anthropic dev on retainer)
• Revenue share (% of pilot fees)
• Co-marketing (Claude reference for enterprise AI safety)

Is anyone from the Claude Code team interested in chatting about this?

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cc @anthropics/claude-code-team