DigitalNet.ai Released Enterprise AI Security Paper
The document outlines new architectural safeguards for businesses using AI governance platforms.
Updated on Sept. 21, 2026 in Artificial Intelligence
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DigitalNet.ai released a paper titled Slowing Down Is Not a Control that details critical enterprise AI risks. The study addresses security vulnerabilities like data exposure and hallucination in automated agents.
Why it matters
Current agent platforms that rely on language models for decision-making can allow agents to bypass instructions. Architectural safeguards are necessary to prevent unauthorized actions and data leakage in enterprise settings.
The JanusAI platform utilizes deterministic methods for decision-making rather than standard language models. ATLAS scores identities based on over 50 factors to ensure secure execution.
The players
DigitalNet.ai
DigitalNet.ai is a technology firm based in Bethesda, Md., that focuses on developing governance platforms for enterprise artificial intelligence.
JanusAI
JanusAI is a specialized DigitalNet.ai platform designed to manage AI governance through deterministic decision-making processes.
The details
JanusAI enforces role-based and attribute-based access control directly within the execution path. The Zeus component decomposes complex goals and authorizes individual actions to maintain a comprehensive audit trail, while mapping activity to control families like FedRAMP and SOC 2.
Timeline
September 21, 2026: DigitalNet.ai released the point-of-view paper.
The Tech Race
By mapping activity to established standards like the FedRAMP security framework, this approach signals a shift toward treating AI agents as standard IT infrastructure. This integration moves beyond legacy language model controls to compete in the growing market for secure corporate automation.
Business users can expect more rigid, rule-based interactions with AI agents rather than unpredictable natural language outputs. This shift prioritizes security and accountability, requiring employees to align with structured access controls and identity scoring.
The takeaway
Enterprise AI requires moving beyond basic language model prompts toward deterministic, rule-based systems to ensure data safety. Companies should audit their agent platforms for hard-coded governance rather than relying on the generative model to police itself.
Further reading
For more on the evolving security landscape for machine learning, visit the Artificial Intelligence section.
More information
Access the detailed findings in the full point-of-view paper on AI risks.
Source note: This article includes information reported by The Montreal Gazette.
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Do you trust that businesses can reliably control autonomous AI systems to prevent security risks?
