Security and Privacy

As AI systems become deeply embedded in energy analytics and customer engagement workflows, EcoMetricx stands at the forefront of secure, privacy-preserving data innovation.

Our Approach

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.

Privacy-by-design framework aligned with NIST CSF 2.0, CSA STAR CCM v4, and California CPUC privacy regulations
Secure ingestion pipelines, anonymized analytics, and privacy-protected AI services
Layered protections—including role-based access, homomorphic encryption, differential privacy, and federated learning
Synthetic data generation for safe innovation testing
Secure multiparty computation for joint analytics across institutions
Encode all sensitive variables before transmitting prompts to large language models (LLMs)

Core Service Areas

Differential Privacy Layer for Statistical Queries

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.

Federated Model Training Without Data Exposure

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.

Synthetic Data Generation for Safe Prototyping

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.

Pre-Encoded AI Prompt Pipelines

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.

Homomorphic Encryption for Encrypted AI Workloads

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.

Secure Multiparty Computation (SMPC)

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.

Auditability and Governance for AI Behavior

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.

Anomaly Detection in Agentic AI Systems

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.

Zero-Trust AI Integration for Chatbots and 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.

Regulatory-Aligned Privacy Architecture

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.

Core Service Areas

Differential Privacy Layer for Statistical Queries

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.

Federated Model Training Without Data Exposure

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.

Synthetic Data Generation for Safe Prototyping

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.

Pre-Encoded AI Prompt Pipelines

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.

Secure Multiparty Computation (SMPC)

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.

Homomorphic Encryption for Encrypted AI Workloads

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.

Auditability and Governance for AI Behavior

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.

Anomaly Detection in Agentic AI Systems

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.

Zero-TrustAI Integration for Chatbots and 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.

Regulatory-Aligned Privacy Architecture

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.

Whitepapers

WHITEPAPER
A Privacy-First Blueprint for
Secure Energy Innovation: EcoMetricx in Action
Andrei Ionete and Alex Ledbetter
View Whitepaper
WHITEPAPER
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations
Benjamin Westrich, MSc
View Whitepaper
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