LEDGERsigned · hash-chained · verifiable offlinePOLICIESplain english · compiled · enforced inlineRED TEAMcontinuous adversarial probing on shadow twinsCOMPLIANCESOC 2 · GDPR · HIPAA · PCI-DSS · EU AI Act · ISO 42001INTEGRATIONgateway · SDK · sidecar · no rewritesLEDGERsigned · hash-chained · verifiable offlinePOLICIESplain english · compiled · enforced inlineRED TEAMcontinuous adversarial probing on shadow twinsCOMPLIANCESOC 2 · GDPR · HIPAA · PCI-DSS · EU AI Act · ISO 42001INTEGRATIONgateway · SDK · sidecar · no rewrites
Teleon / Blog

Agentic security, in plain terms.

Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.

AI Governance

Governance for Internally Built AI Agents

Internally built agents need the same governance rigor as vendor tools, since being built in-house does not reduce risk and often reduces external scrutiny.

AI GovernanceSeptember 23, 2026
AI Governance

AI Management Systems and ISO/IEC 42001

ISO/IEC 42001 provides a dedicated management system structure for AI specifically, useful for organizations whose AI risk has outgrown a bolt-on approach.

AI GovernanceSeptember 23, 2026
AI Governance

AI Risk Considerations for ISO 27001 Programs

ISO 27001's risk-based structure absorbs AI risk naturally, as long as AI-specific threats are actually included in the risk assessment scope, not assumed to be covered.

AI GovernanceSeptember 23, 2026
AI Governance

AI Security Controls for SOC 2 Programs

AI systems fit into existing SOC 2 trust service criteria, but auditors increasingly expect AI-specific evidence within those criteria, not a generic response.

AI GovernanceSeptember 23, 2026
AI Governance

AI Governance for Regulated Industries

Regulated industries need AI governance mapped explicitly onto their existing regulatory obligations, since a generic AI governance program alone will not satisfy them.

AI GovernanceSeptember 23, 2026
AI Governance

How to Design an AI Exception Process

An AI exception process needs a specific owner, a time limit, and a documented reason, or exceptions quietly become the new unreviewed default instead of a controlled deviation.

AI GovernanceSeptember 23, 2026
AI Governance

Human Oversight for High-Risk AI Actions

Human oversight only works if the human reviewing an action has enough specific context to catch a bad one, which most rubber-stamp approval flows fail to provide.

AI GovernanceSeptember 23, 2026
AI Governance

Why AI Decisions Need Traceability

Traceability connects an AI system's output back to the specific model, prompt, data, and policy decision behind it, which matters most exactly when something goes wrong.

AI GovernanceSeptember 23, 2026
AI Governance

Evidence Collection for AI Governance

AI governance evidence needs to be built into normal operation as a byproduct, not assembled reactively when an auditor or regulator asks for proof.

AI GovernanceSeptember 23, 2026
AI Governance

Third-Party AI Vendor Security Assessment

Assessing an AI vendor needs AI-specific questions about training data use, retention, and model behavior, beyond the general security questionnaire already in use.

AI GovernanceSeptember 23, 2026
AI Governance

Shadow AI: Risks, Detection, and Governance

Shadow AI, tools and integrations adopted without central visibility, is best addressed by making sanctioned tools easier to use, not by relying on bans alone.

AI GovernanceSeptember 23, 2026
AI Governance

How to Create an Enterprise Acceptable Use Policy for AI

An enterprise AI acceptable use policy needs specific, checkable rules by use-case category, not general values, and a clear path from each rule to its enforcement.

AI GovernanceSeptember 23, 2026