Control what your agents can access. Define what they're authorised to do. Monitor them in real time and produce the audit trail when your regulator asks. Detect Patterns of Malice before they become incidents.
One question opens every conversation: "When your regulator asks how you govern your AI agents. what do you show them?"
AI agent generates investment recommendations for retail customers. Purser verifies each recommendation was within the agent's authorised scope and the customer's approved risk profile, before it reaches the customer.
AI trading agent executes a pattern of trades that individually appear within parameters but collectively represent position accumulation outside its mandate. Purser detects sequential intent drift and flags before limits are breached.
Customer-facing AI agent begins offering terms it was not authorised to make. expanding its scope through accumulated session context. Purser detects in-chain intent misalignment and escalates before the commitment is made.
Payments agent processes transactions that individually pass AML screening but collectively show structuring patterns. Purser detects behavioural anomaly across the sequence and holds for human review.
EU-regulated trading desk must document governance of AI-generated algo models. Purser defines the authorised intent envelope for each model and continuously monitors for parameter drift. satisfying RTS6 supervisory obligations.
Research agent autonomously generates equity research consumed by advisors. Purser verifies every research output stayed within the agent's authorised analytical mandate and flags any scope violations before publication.
Traditional security looks for Indicators of Compromise — known bad signatures, flagged content, explicit rule violations. Purser detects something harder to find and more dangerous: the pattern of agent actions that individually appear authorised but collectively signal intent drift, scope creep, or adversarial manipulation.
We call these Patterns of Malice. Not a single bad action — a sequence of plausible actions whose aggregate trajectory reveals that something has gone wrong. The same insight that makes fraud detection powerful, applied to autonomous agent behaviour in regulated environments.
An agent makes a series of individually authorised decisions that collectively accumulate outside its mandate. No single action triggers a rule. The pattern does.
Agent actions begin diverging from the authorised business intent defined at deployment. Semantically similar to authorised behaviour. Statistically anomalous against the baseline.
External inputs — tool outputs, retrieved documents, API responses — subtly shift agent reasoning away from its authorised mandate. Prompt injection at the context layer, not the prompt layer.