Our AI data security services are built on a privacy-by-design framework aligned with NIST CSF 2.0, CSA STAR CCM v4, and California CPUC privacy regulations. We support utility clients, regional energy networks, and energy innovators in managing sensitive data by offering secure ingestion pipelines, anonymized analytics, and privacy-protected AI services. Our architecture incorporates layered protections—including role-based access, homomorphic encryption, differential privacy, and federated learning—to ensure that no personally identifiable information is exposed to third-party models or external compute environments. We use synthetic data generation for safe innovation testing, secure multiparty computation for joint analytics across institutions, and encode all sensitive variables before transmitting prompts to large language models (LLMs).
With capabilities spanning risk governance, anomaly detection, and secure chatbot design, EcoMetricx helps clients operationalize AI without compromising trust, regulatory compliance, or customer transparency.
Whether deploying demand response models, forecasting tools, or interactive AI agents, our systems are engineered to safeguard identity, consent, and data integrity across the full AI lifecycle.
EcoMetricx integrates formal (ε, δ)-differential privacy into data outputs used in reports and LLM applications. This ensures provable protection against re-identification attacks, even when AI agents access aggregate usage patterns.
We train machine learning models collaboratively across utilities or customer segments using federated learning, keeping raw data on-premise and sharing only encrypted model updates to mitigate privacy risk in decentralized AI deployments.
EcoMetricx produces high-fidelity, non-identifiable synthetic datasets that mirror real usage behavior, enabling AI testing, algorithm development, and sandbox experimentation without exposing live customer records.
Sensitive user data is transformed and encoded prior to being passed into large language models (LLMs), ensuring that AI agents operate only on secure abstractions rather than raw personal information.
Encrypted energy usage data can be processed by AI models in cloud environments without ever decrypting the data, leveraging partial or fully homomorphic encryption to secure sensitive inferences.
We support joint AI analytics (e.g., multi-utility load forecasting or DER optimization) using cryptographic SMPC protocols, so each party can compute a result without exposing private datasets to others.
Every AI decision, model output, and cross-system interaction is logged in immutable audit trails to satisfy FIPPs accountability requirements. These logs can be mapped to policy rules and risk thresholds.
We implement real-time alerts for unusual agent behavior, such as unexpected escalation actions or data access patterns, supporting safe deployment of autonomous or semi-autonomous AI agents.
Our chatbot and AI agent architectures follow zero-trust principles: least-privilege access, sandboxed environments, prompt sanitization, and embedded consent mechanisms ensure privacy by design.
All AI data workflows comply with CPUC Decisions D.11-07-056, D.11-08-045, CCPA, and GDPR. We offer utilities and CCAs an actionable compliance pathway through a modular privacy engine framework.