Hidden instruction chains
Detects concealed prompt sequences that attempt to redirect the AI away from the user’s visible intent.
DotShield™ Involucrum is being developed to assess interaction patterns, intent divergence, and adversarial prompt behaviour before sensitive prompts reach external AI systems.
Intent divergence: the visible request appears harmless, but the prompt structure begins to drift toward a different operational objective.
Hidden instructions: concealed directives attempt to influence the AI response without being obvious in the visible text.
Data exfiltration: sensitive identifiers, business records, or regulated data may be requested or routed in ways the user did not intend.
AI misuse patterns: repeated interaction behaviour may suggest unsafe automation, policy bypass, or unauthorised workflow manipulation.
Involucrum identifies covert misuse patterns that standard controls cannot see — across cybersecurity operations, healthcare, legal environments, and other high-trust workflows where AI is becoming part of daily decision-making.
Detects concealed prompt sequences that attempt to redirect the AI away from the user’s visible intent.
Flags attempts to leak sensitive, privileged, regulated, or commercially confidential information.
Identifies patterns that attempt to alter business decisions, approvals, payments, or instructions.
Looks beyond keywords to detect structural, behavioural, and interaction-level misuse patterns.
Involucrum helps identify interaction-layer risks that ordinary prompt filters may miss.
Its behaviour is validated through controlled proof-of-concept testing, evidence logs, and customer-specific risk scenarios.
Every tier starts with on-device detection. The deployment model determines how prompts are protected, where encryption is controlled, and how much of the network attack surface is reduced.