Machine Learning / Artificial Intelligence
Security

Protect machine learning systems and LLM deployments from adversarial, privacy, and supply-chain risks.Syiert combines operational experience, AI research, and secure engineering to deliver model-safe production systems.

Why ML / AI Security Matters

Modern organizations rely on ML models and large language models for decision-making. These systems introduce new risks:

  • Adversarial attacks — crafted inputs to manipulate model output.
  • Data poisoning & tampering — corrupted training data.
  • Model inversion & membership inference — leaking sensitive training data.
  • Prompt injection (LLMs) — malicious instructions embedded in user prompts.
  • Model theft & supply-chain risk — unauthorized model exfiltration.

Fast Facts

Syiert combines AWS-native tooling with in-house defenses (adversarial training, Trust Score, monitoring). Enterprises achieve secure, auditable ML operations and compliance readiness. 🔐⚡

With Syiert, enterprises can accelerate AI adoption while meeting strict compliance standards such as FedRAMP, HIPAA, and GDPR. 📜✅

Syiert’s Trust Score framework continuously evaluates model reliability, ensuring AI decisions are transparent, explainable, and audit-ready. 📊🔎

Our Approach — Practical & Repeatable

  1. Data Governance & Provenance: ingest validation, dataset versioning, immutability for training data, access controls, and KMS encryption.
  2. Model Hardening: adversarial training, input sanitization, certified defenses where applicable, and LLM prompt-filtering layers.
  3. Secure Dev & CI/CD: container image signing, SBOM, IaC scanning, model registry (versioned), and gated promotion to production.
  4. Access Controls & Secrets: fine-grained IAM roles, temporary credentials, Secrets Manager, and least-privilege ML endpoints.
  5. Monitoring & Detection: concept-drift detection, data-quality alerts, inference anomaly detection, explainability telemetry, and centralized logging.
  6. Governance & Incident Response: model risk scorecards, runbooks for model rollback, and evidence packages for audits (FedRAMP / CMMC readiness).

Recommended Production Architecture (AWS)

Syiert implements hardened pipelines from data collection through model serving. Key components:
KMS for encryption, Secrets Manager, SageMaker Model Registry,
VPC Endpoints, CloudWatch & SIEM integration.


Data Ingest → S3 (encrypted, versioned) → Glue / Dataprep → Feature Store
→ Training (SageMaker within VPC, IAM roles)
→ Model Registry & Signed Artifacts
→ Model Endpoint (SageMaker / ECS behind ALB)
→ Monitoring: CloudWatch, GuardDuty, Custom Drift/Anomaly Detectors
→ Audit & Governance: Logs, Model Card, SBOM, Evidence for compliance

Compliance & Governance

We map ML controls to regulatory frameworks—FedRAMP, CMMC, NIST AI Risk Management Framework—and produce evidence bundles for audits. Governance includes model cards, data lineage, and approved access control matrices.

Interested in a security review of your ML / LLM systems?

Schedule a technical consultation or request a pilot assessment.

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