AI Usage Policy

Introduction

Tactical Edge AI (“Tactical Edge,” “we,” “us,” or “our”) builds and deploys artificial intelligence systems for enterprise clients. AI is central to our products, services, and consulting engagements.

This AI Usage Policy outlines how we develop, deploy, and govern AI systems across our organization. It reflects our commitment to building AI that is transparent, fair, accountable, and aligned with the interests of our customers, their users, and the broader public.

How We Use AI in Products and Services

AI powers the core capabilities of our products and consulting engagements, including:

• Agentic AI systems that reason, decide, and act autonomously within defined enterprise workflows
• Large Language Model (LLM) integrations for natural language understanding, generation, and summarization
• Predictive analytics and recommendation engines across sales, operations, and customer engagement
• Intelligent document processing and knowledge extraction
• Voice AI agents for contact center automation and customer interaction
• Custom model fine-tuning and domain-specific model development for regulated industries

Responsible AI Principles

Every AI system we build or deploy is governed by five core principles:

• Transparency: We clearly communicate when AI is being used, what data it processes, and how decisions are made. Users and stakeholders can understand the role AI plays in any interaction or outcome.
• Fairness: We design and test AI systems to avoid unjust bias and discrimination. Outputs are evaluated for equitable treatment across demographic groups and use cases.
• Accountability: Every AI system has a designated owner responsible for its behavior, performance, and compliance. We maintain clear audit trails and escalation paths.
• Privacy: AI systems are designed with privacy by default. We minimize data collection, enforce access controls, and comply with applicable data protection regulations.
• Safety: We evaluate AI systems for potential harms before deployment and implement safeguards, guardrails, and fallback mechanisms to prevent unintended consequences.

Human Oversight and Governance

AI systems at Tactical Edge operate under structured human oversight:

• High-stakes decisions always include human-in-the-loop review before execution
• Autonomous agents operate within clearly defined permission boundaries and escalation policies
• An internal AI Governance Committee reviews new AI deployments, evaluates risk levels, and approves production releases
• Regular model performance reviews ensure continued alignment with intended behavior
• Kill switches and rollback mechanisms are implemented for all production AI systems

Data Handling in AI Systems

Data used in our AI systems is handled with strict controls:

• Customer data is never used to train models serving other customers unless explicitly authorized
• Training data is reviewed for quality, relevance, and potential bias before use
• Personally identifiable information (PII) is anonymized or pseudonymized wherever possible in AI pipelines
• Data retention policies specific to AI training and inference are enforced separately from general data retention
• Access to AI training data and model artifacts is restricted by role-based access controls

Model Selection and Evaluation

We select and evaluate AI models based on rigorous criteria:

• Task-specific performance benchmarks and accuracy thresholds
• Latency, cost, and scalability requirements for production workloads
• Security posture and data handling practices of model providers
• Licensing terms, intellectual property considerations, and compliance with customer contractual obligations
• Interpretability and explainability of model outputs for the intended use case
• Ongoing monitoring of model drift, degradation, and emergent behaviors in production

Bias Monitoring and Mitigation

We actively work to identify and reduce bias in our AI systems:

• Pre-deployment bias audits are conducted on training data and model outputs
• Fairness metrics are defined and tracked for each AI system based on its use case and affected populations
• Feedback mechanisms allow users and stakeholders to report potential bias or harmful outputs
• Identified biases trigger remediation workflows including data rebalancing, prompt engineering adjustments, or model retraining
• Third-party audits may be engaged for high-risk AI applications

Customer Data Protection in AI Pipelines

Protecting customer data within AI processing pipelines is a non-negotiable commitment:

• All data in AI pipelines is encrypted in transit and at rest
• Customer data environments are logically or physically isolated
• Inference logs containing sensitive data are subject to retention limits and access controls
• Model outputs are not cached or stored beyond the session unless explicitly configured by the customer
• Data processing agreements (DPAs) govern AI-related data handling for enterprise engagements

Compliance with Emerging AI Regulations

We monitor and adapt to the evolving global regulatory landscape for AI, including:

• The EU AI Act and its risk-based classification framework
• US federal and state AI governance requirements, including executive orders and sector-specific guidance
• International standards such as ISO/IEC 42001 (AI Management Systems) and NIST AI Risk Management Framework
• Industry-specific regulations for AI in healthcare, finance, and government sectors

Our governance processes are designed to be adaptable so that compliance obligations can be met as regulations are enacted and evolve.