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ARGUS : Dual model verification by default
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GenomixIQ : Clinical genomics data ingestion
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ARIA RCM : Healthcare revenue cycle workflows
All production systems in regulated environments.
Thursday: Agent firewalls are the next layer
Agent firewalls enforce security policies traditional infrastructure can't.
What they block
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Instruction injection: Override commands
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Credential exfiltration: Data to external endpoints
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Privilege escalation: Unauthorized tool calls
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Decision manipulation: Logic chain redirects
Five-layer architecture
Layer 1: Input validation
- Markdown sanitization
- Suspicious URL redaction
- Pattern matching for attack signatures
Layer 2: Instruction detection
- ML models trained on override attempts
- Recognizes semantic patterns (role reversals, system prompt refs)
Layer 3: Permission checks
- Compartmentalized tool authorization
- Research agents: read only
- Write agents: database access, no email
- Email agents: no payment processing
Layer 4: Decision logging
- Full audit trails with context
- Source data tracking
- Reasoning chain capture
- Forensic reconstruction capability
Layer 5: Human confirmation gates
- Financial transactions require approval
- Data deletion needs review
- Credential changes trigger verification
Zero trust for agents
Never trust input. Assume web content hostile. Verify every action. Log decision lineage. Compartmentalize tools. Human in loop for high stakes.
Friday: Five questions before deployment
Does your sanitizer have zero system permissions?
If your sanitizer can write to databases or send emails, it's not a sanitizer. It's a production agent reading untrusted input. When compromised, attackers gain those capabilities.
Are tool permissions compartmentalized by role?
Monolithic access = single compromised agent exposes entire system. Implement RBAC for agents.
Can you reconstruct every decision from logs?
If compliance asks why an agent made a recommendation 6 months ago, can you trace to exact data sources and reasoning steps?
Does human confirmation trigger for financial actions?
Agents processing payments without approval = automated embezzlement risk. Confirmation gates are not optional.
Have you tested injection attacks?
No red team testing = you don't know if defenses work. Run adversarial testing continuously.
The 86-89% that fail discover these requirements 6 weeks before go-live when compliance asks.
The 14% that succeed build them day one.
What this means for your systems
Security architecture requirements:
✅ Dual model verification - Sanitizer + production agent separation
✅ Compartmentalized permissions - Role-based tool access
✅ Decision lineage tracking - Full audit trails
✅ Human confirmation gates - Required for high-stakes actions
✅ Continuous injection testing - Red team + automated
Not optional enhancements. Production requirements.
Resources
AI Aether : Free agent security readiness assessment (30 min, 30 questions)
ARGUS : Dual model verification, available on PyPI/GitHub
GenomixIQ : Clinical genomics with FHIR R4 interoperability
ARIA RCM : Healthcare revenue cycle with HIPAA compliance
All production-grade. No pilots. No POCs. Systems that ship and scale.
Years production AI taught one lesson
The teams that succeed build governance before deployment, not after compliance review.
RCMTech: 340ドルM measurable improvements, 89 days integration, zero clinical data loss
GeneticsTech: 99.97% uptime during 50TB migration, FHIR R4 compliance throughout
EnergyTech: 23→81% AI adoption among 20-year veteran operators
HealthTech: Petabyte-scale platforms, every decision traceable
Anil Prasad is Founder of Ambharii Technologies and Head of Engineering & Product at EnergyTech.
28 years building production AI in regulated environments across Fortune 100 companies. Currently building agent security infrastructure for enterprise AI: dual-model verification, compartmentalized permissions, and audit trail architecture for autonomous systems.
Connect: LinkedIn | Website | GitHub
Next week: Production deployment patterns, compliance architecture, audit trail infrastructure.
AgentSecurity #EnterpriseAI #HumanWritten #ExpertiseFromField