The dark side of enterprise AI

One of the dark sides of enterprise AI is that as AI gets into enterprise workflows, the problem of deploying AI securely is rising up in the to-do list for tech teams. A layered cybersecurity model layered on the infrastructure layer, access control layer, sanitization to stop injection attacks, and backend-only inference is being used by one organization. In an in-depth technical interview with Vardhineni Sowjanya Rani, Senior Platform Security Officer, Amara Ai shares how her team provides end-to-end protection and auditability of AI propositions.

AI designed with defense in mind

The system architecture we have implemented isolates all public-facing services from our internal computation and data layers. Incoming traffic traverses through load balancers and edge gateways before hitting isolated private subnets. Actual sensitive activity, like model inferences and database access with the possibility of Basel event capture, is from an external attack surface. From a security perspective, there is essentially one bastion host enforcing defined IAM roles for administrative access and maintaining audit logs.

Managing identity and internal access

Access starts with JSON Web Tokens (JWTs) with role metadata. Enterprise-level SSO and MFA required. All permissions are defined through RBAC and managed across AWS services. Secrets and tokens are stored in AWS Secrets Manager, not in code or environment variables.

Neutralizing adversarial inputs

All user data is preprocessed before it hits the model. PII is redacted automatically. Users can’t create custom prompts—instead, fixed backend templates are used to control input. This prevents prompt injection and other adversarial inputs.

Backend-only inference and no data retention

No AI models are trained or hosted internally. Inference is done through external APIs that don’t store or train on user data. These APIs are only called from backend systems, so keys and logic are not exposed to the client. Each request is logged for tracing.

Monitoring infrastructure and usage

Amazon CloudWatch is used to monitor server health, latency, and access volume. If abnormal activity is detected, alerts are sent to the engineering response team via Amazon SNS. Every call to external AI services is logged with metadata like prompt category and latency for analysis.

Post-inference filtering

AI-generated outputs are validated through multiple checks before they hit the user. The system looks for hallucinations, data leaks, and other signs of unsafe output. Problematic responses are blocked or logged for review. All responses are tied to session-level data for full traceability.

Compliance and future-proofing

SOC 2 Type II and ISO/IEC 27001:2013 are up to date and audited. We can provide multiple security compliance assessments for SOC 1, SOC 2, GDPR, HIPAA, and contractual compliance, and we will continue to learn as quantum-resistant cryptography, NIST’s AI Risk Management Framework, and OWASP’s LLM Top 10 emerge as areas of interest for future compliance.

Final Note

Building secure AI systems is more than just working with basic isolated controls; it’s a matter of having a consistent focus across the infrastructure, the access, the data flow, and the inference logic. The architecture we showed can help organizations ignore some of the darker sides of using AI while still hitting security and compliance requirements.

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