White Paper | 2026-03-27 | Status: draft
Agentic Transaction Security Framework
Executive Summary
The integration of autonomous AI agents into enterprise financial workflows has progressed from a theoretical concern to an operational reality. As of early 2026, agents built on large language models are being deployed across procurement, accounts payable, expense management, subscription management, and trading workflows with varying degrees of human supervision. These systems can initiate, approve, and record financial transactions — wire transfers, card charges, purchase orders, cryptocurrency transfers — at speeds and volumes that fundamentally exceed the capacity of human oversight mechanisms designed for previous generations of automation [1].
The security and compliance infrastructure governing these transactions has not kept pace. PCI DSS v4.0.1, which became fully mandatory on March 31, 2025, provides important guidance on non-human identity management and automated system authentication, but its control framework was not designed to address the goal-directed, context-sensitive behavior of agentic systems [2]. PSD2’s Strong Customer Authentication requirements create structural tension with autonomous operation: the regulation’s mandate that payment authentication rely on elements “the payer is, knows, or possesses” assumes a human payer, while an AI agent possesses none of these in any meaningful regulatory sense [3]. The European Parliament and Council reached provisional political agreement on PSD3 and the Payment Services Regulation in November 2025, with the PSR expected to enter force by April 2026, yet neither instrument explicitly addresses agentic payment execution [4]. KYC and AML frameworks face a parallel challenge: when an agent initiates a transaction, identifying the beneficial owner and attributing intent to a specific human principal requires new procedural and technical infrastructure that most organizations have not built [5].
The liability picture is equally unsettled. Courts have not yet issued definitive rulings allocating responsibility between agent developers, deploying organizations, and platform providers for unauthorized agent-initiated transactions, and contractual frameworks for managing this exposure remain immature. The EU’s new Product Liability Directive, to be implemented by EU member states by December 2026, explicitly includes AI software as a product subject to strict liability if found defective — a classification that could significantly alter the risk calculus for agent developers whose systems initiate unauthorized financial transfers [6].
This whitepaper presents the Agentic Transaction Security Framework (ATSF), a practical governance structure for organizations deploying agents with transaction authority. The framework addresses five interconnected problem areas: verifying that agent-initiated transactions reflect genuine human intent; structuring authorization workflows that remain secure under autonomous operation; adapting fraud detection to agentic behavioral patterns; achieving compliance with applicable payment regulations; and allocating liability clearly through technical controls and contractual mechanisms. Each section maps framework components to relevant regulatory requirements and control frameworks, with a consolidated alignment table in Section 9.
1. Introduction: The Agent-as-Transactor Problem
Financial automation is not new. Electronic funds transfer systems have operated since the 1970s, and rule-based robotic process automation has handled routine payment workflows for decades. What distinguishes agentic AI from earlier automation is not the capacity to execute transactions — it is the capacity to decide what transactions to execute. An RPA bot initiates a payment when a specific trigger condition is met, following a predetermined logic tree that a human designed and approved. An AI agent initiates a payment because it has determined, based on contextual reasoning over a high-level objective, that the payment is the appropriate next action toward an organizational goal [7].
This distinction has profound security implications. A rule-based automation system can be secured by auditing its rules: if the rules are correct and the inputs are validated, the outputs are predictable. An agentic system is secured by auditing its intent alignment and its authorization boundaries, both of which are significantly harder to specify and verify. The agent may encounter novel situations its developers did not anticipate; it may interpret its mandate more broadly than the human principal intended; it may be manipulated through adversarial inputs that redirect its goal — a class of attack the OWASP Top 10 for Agentic Applications 2026 categorizes as ASI01 (Agent Goal Hijack) [8]. And because the agent operates autonomously over extended periods, the window between when a problematic transaction is initiated and when a human becomes aware of it may be measured in hours or days rather than seconds.
The scale and velocity of potential harm distinguishes agentic transactions from traditional automated fraud scenarios. A stolen credit card placed on file with an AI shopping agent could drive a rapid series of unauthorized purchases across multiple merchants before velocity controls trigger, generating disputes that the payment network’s chargeback infrastructure was not designed to handle at this volume [1]. In enterprise contexts, a procurement agent with supplier payment authority whose goal has been hijacked through indirect prompt injection — malicious instructions embedded in a vendor’s invoice PDF, for example — could initiate wire transfers to attacker-controlled accounts before any monitoring system identifies the anomaly [9]. In 2025, AI-enabled financial scams increased by roughly 500 percent year over year, with illicit crypto volume reaching $158 billion, illustrating the broader threat environment within which agentic payment systems now operate [10].
Existing security frameworks provide a starting point but not a complete answer. PCI DSS v4.0.1’s Requirements 7 and 8 address non-human identity management and least-privilege access in ways that are directly relevant to agent credential governance [2]. PSD2 and the forthcoming PSD3/PSR framework establish consent and authorization models that, while designed for human payers, can be adapted for agent-delegated transaction authority with appropriate engineering [3]. KYC and AML regulations impose beneficial ownership identification and suspicious activity monitoring obligations that organizations must re-examine when the initiating entity is a software agent rather than a natural person [5]. The ATSF does not replace these frameworks; it provides a structured integration layer that helps organizations implement them coherently for agentic contexts.
The remainder of this paper proceeds as follows. Sections 2 through 4 address the three core technical challenges — intent verification, authorization architecture, and fraud detection — that must be solved before any compliance mapping is meaningful. Sections 5 through 8 address regulatory compliance, liability allocation, transaction monitoring, and dispute resolution respectively. Section 9 provides a consolidated framework alignment table.
2. Intent Context Verification
Every transaction initiated by an autonomous agent traces back to a human intent, however remotely. A chief financial officer who instructs an AI agent to “manage our vendor payment queue and ensure all approved invoices are paid within terms” has expressed a genuine organizational objective; the agent’s subsequent payment actions are, in principle, authorized by that instruction. The security challenge is that the chain from human intent to specific transaction is often long, indirect, and susceptible to manipulation at multiple points. Intent context verification is the set of technical and procedural mechanisms that preserve and validate that chain throughout the agent’s operation.
The foundational principle of intent context verification is that every agent-initiated transaction must be traceable to a specific human authorization event that is temporally proximate to the transaction, scoped appropriately to the transaction’s risk level, and cryptographically attributable to an authenticated human principal. This principle distinguishes agentic payment security from traditional delegation models where a human operator delegates broad authority once and automated systems exercise it indefinitely. The duration and scope of an agent’s delegated transaction authority must be bounded, and those bounds must be technically enforced rather than merely stated in policy [11].
A practical implementation of intent context verification begins with structured intent capture at the point of task assignment. When a human principal assigns a transaction-relevant task to an agent, the assignment should be recorded as a signed intent record containing: the principal’s authenticated identity, a natural language description of the authorized objective, explicit transaction scope parameters (permitted counterparties, maximum transaction values, permitted payment methods, time bounds), and a cryptographic commitment that binds the agent session to this specific authorization event. This intent record functions as the authorization envelope within which the agent operates; all subsequent transaction requests must be validated against it before execution. Importantly, the intent record is created and signed by the human principal, not generated by the agent itself — closing the class of attacks where an agent manipulates its own authorization context [12].
Threshold-based human confirmation provides a second layer of intent verification for high-value or anomalous transactions within an authorized session. Organizations should establish risk-tiered confirmation thresholds calibrated to their specific transaction profiles: for example, transactions within predefined parameters may proceed autonomously, while transactions approaching a defined percentage of the session’s maximum authorized value or involving first-time counterparties require synchronous human confirmation before execution. The confirmation mechanism must be implemented out-of-band relative to the agent’s reasoning context — a confirmation request sent through a separate, authenticated channel that the agent cannot influence — to prevent a manipulated agent from generating false confirmations [13]. Visa’s research on agentic AI fraud impact confirms that payment infrastructure must adapt to these verification architectures, as traditional authorization flows assume human decision-making at the point of payment submission [14].
Technical implementation of intent context verification requires several components working in concert. The agent runtime must maintain a session-scoped authorization token that encodes the intent record parameters and is checked at each tool invocation that could result in a financial commitment. This token must be issued by an authorization service that is architecturally separate from the agent’s planning and reasoning components, so that a compromised agent cannot forge or modify its own authorization scope. Cryptographic binding between the token and the authenticated human principal’s identity ensures that authorization cannot be transferred between agents or sessions without a new human authentication event. Organizations deploying agents in payment-relevant workflows should implement these controls before granting transaction authority, treating the authorization infrastructure as a prerequisite rather than an enhancement.
3. Multi-Party Authorization Models
The principle of dual control — requiring two independent human approvals for high-value financial transactions — is foundational to payment security and is embedded in the control frameworks of every major financial institution. When one or both of those approving parties is an AI agent, the security properties of dual control must be reconsidered from first principles. An agent’s “approval” of a transaction is a reasoning output, not an independent judgment: if two agents derive their reasoning from the same underlying model or share an information context that has been manipulated, they may reach the same incorrect conclusion regardless of how the authorization workflow is structured. Multi-party authorization for agentic systems must therefore be designed to provide genuine independence, not merely process compliance.
The architecture of multi-party authorization for agent-initiated transactions should follow a tiered model based on transaction value and risk profile. At the lowest tier — routine, pre-approved transaction types within established parameters — a single agent may initiate and execute with only the intent context verification controls described in Section 2. At the middle tier — transactions approaching maximum authorized values, first-time counterparties, or transactions outside standard business hours — a second verification step is required from either a second independent agent or a human reviewer. At the highest tier — transactions above a defined absolute value threshold, transactions to accounts that have not previously received payment, or any transaction where the agent’s confidence score falls below a configured threshold — synchronous human authorization is mandatory before execution [15].
The design of the middle tier requires careful attention to independence. When using agent-to-agent authorization, the reviewing agent must be architecturally isolated from the initiating agent: it must receive only a structured transaction summary (amount, counterparty, payment method, stated business justification), not the initiating agent’s full reasoning trace or the task context that could carry adversarial manipulation. The reviewing agent should be configured with a conservative mandate — “flag any transaction that is inconsistent with standard payment patterns for this organization” — rather than a permissive one. Inter-agent authorization messages must be signed and transmitted through an authenticated channel, guarding against the ASI07 (Insecure Inter-Agent Communication) risk category identified by OWASP [8]. When the reviewing entity is human, the confirmation interface must present the transaction in a standardized format that makes anomalies visible — flagging first-time counterparties, unusual amounts, and off-schedule timing — rather than relying on the reviewer to identify these characteristics independently.
Smart authorization workflows can automate the routing of transaction requests to the appropriate authorization tier without requiring human configuration at each request. These workflows function as a transaction policy engine that evaluates each pending agent transaction against a rule set encoding the tiering logic, enriched with real-time risk signals from the organization’s fraud detection systems. A transaction that would normally qualify for autonomous execution might be escalated to the middle tier if the counterparty’s account details have changed within the past thirty days, if the transaction falls within a time window historically associated with fraud in the organization’s sector, or if the organization’s threat intelligence feed has flagged the payment network being used. The policy engine is separate from the agent’s reasoning environment, maintains its own audit log, and cannot be overridden by agent-generated instructions — only by authenticated human administrators [16].
Smart contract mechanisms on permissioned blockchain infrastructure offer an alternative authorization model for multi-institutional workflows, particularly in supply chain finance and cross-border payment scenarios. In these architectures, transaction authorization conditions are encoded in a smart contract that evaluates conditions from multiple authorized parties before releasing a payment. An agent may submit a payment request to the contract, but the contract will not execute until all required authorization signatures — which may include those from a human treasury officer, a second agent operating under different credentials, and an external counterparty confirmation — have been received and validated. This approach provides a cryptographic audit trail of the authorization chain that survives the lifecycle of any individual agent session and can be produced in regulatory examinations or dispute proceedings [17].
4. Fraud Detection for Autonomous Transaction Patterns
Traditional payment fraud detection systems are calibrated against the behavioral signatures of human users. A human making purchases has characteristic patterns: spending occurs during waking hours, transaction velocity matches human browsing and decision speed, counterparties are drawn from a recognizable universe of retail and service merchants, and geographic context correlates with the account holder’s known location. Anomaly detection models trained on these patterns will systematically misclassify legitimate agentic transactions as fraudulent and, more dangerously, may fail to flag fraudulent agentic transactions that superficially resemble the high-velocity, off-hours patterns that agentic operation legitimately produces [18].
The behavioral baseline problem is fundamental to agentic fraud detection. An agent legitimately executing a procurement workflow may initiate twenty-five invoice payments between 2 AM and 4 AM on a Sunday, because the agent has no preference for business hours and may have been scheduled to run during low-traffic periods. The same pattern — high-velocity payments to multiple counterparties during off-hours — is also the signature of a compromised agent executing fraudulent transfers before the activity is noticed. Fraud detection systems must therefore maintain distinct behavioral baselines for human-initiated and agent-initiated transaction streams, and must further distinguish between expected agentic behavior (aligned with the agent’s established operational patterns) and anomalous agentic behavior (deviating from those patterns in ways that suggest manipulation or misconfiguration) [19].
Building agent-specific behavioral baselines requires a structured enrollment process. Before granting an agent production transaction authority, organizations should run the agent through a supervised period — typically two to four weeks of full operation — in a shadow mode where transactions are simulated but not executed. During this period, the fraud detection system builds a behavioral profile encoding the agent’s characteristic transaction velocity, counterparty distribution, payment method preferences, geographic scope, and temporal patterns. The profile is updated continuously during production operation through an online learning process, but updates are gated by a minimum evidence threshold and reviewed by a human security analyst before being incorporated, preventing an adversary from gradually shifting the baseline to accommodate fraudulent patterns [20].
Velocity controls for agentic transactions must be designed differently from human-oriented velocity rules. Standard velocity controls — for example, flagging more than five card transactions in a one-hour window — would be triggered constantly by legitimate high-throughput agent operation. Agent velocity controls should instead be expressed as statistical deviations from the agent’s own baseline: flag when transaction rate exceeds two standard deviations above the agent’s mean hourly rate for the same day-of-week and hour-of-day pattern. Similarly, counterparty novelty scoring — tracking what fraction of an agent’s current transaction batch involves counterparties outside the agent’s established network — provides a more sensitive signal for goal hijacking attacks than absolute velocity metrics. An agent suddenly directing payments to new accounts it has never previously contacted, even at normal velocity, warrants immediate investigation [21].
Several specific fraud patterns are particularly relevant to agentic systems. Beneficiary creep describes the gradual introduction of new payment recipients through prompt injection or context manipulation, where each individual addition appears plausible in isolation but the aggregate effect redirects payment flow to attacker-controlled accounts. Scope inflation describes an agent that progressively interprets its mandate more broadly over time, authorizing larger transactions or a wider counterparty universe than the original intent record specified. Temporal anomaly attacks involve manipulating an agent to execute transactions at times or frequencies inconsistent with its baseline, exploiting the reduced human attention during off-hours. Detection controls for each of these patterns require monitoring at the session and aggregate levels simultaneously, comparing individual transactions against the agent’s own history and comparing session-level patterns against organizational norms [22].
The integration of external threat intelligence strengthens agentic fraud detection substantially. Payment network feeds identifying compromised accounts and suspicious counterparties, industry-shared indicators of agentic compromise from financial sector ISACs, and behavioral threat intelligence specific to the agent frameworks and orchestration platforms in use all provide enrichment that a purely internal baseline system cannot replicate. Organizations deploying agents in payment workflows should establish real-time feeds from these sources and configure their transaction policy engines to cross-reference every pending transaction against current threat intelligence before execution, not merely in post-hoc review [23].
5. Regulatory Compliance Mapping
Agent-initiated financial transactions do not exist in a regulatory vacuum. They are subject to the full body of payment security regulations applicable to the transaction type, the payment network, and the jurisdictions involved — without exception or accommodation for the novelty of the technology. The CFPB has explicitly stated that automated systems and advanced technology do not exempt organizations from consumer financial protection laws, and the PCI Security Standards Council has confirmed that AI systems operating in payment card environments must be implemented in full accordance with PCI DSS regardless of their autonomous characteristics [24]. The following sections map the specific obligations most likely to affect organizations deploying agents with transaction authority.
5.1 PCI DSS v4.0.1
PCI DSS v4.0.1 became the mandatory baseline for all organizations processing, storing, or transmitting payment card data on March 31, 2025, superseding PCI DSS v3.2.1 with no transitional provisions [2]. Several requirements have particular salience for agentic deployments, spanning identity management, access control, and monitoring.
Requirement 8, governing identification and authentication, is the single most directly applicable requirement for agents operating as transactors. PCI DSS 8.2.2 requires that each non-human entity — including automated systems and applications — be assigned a unique identifier so that all actions can be attributed to a specific account, making accountability possible and enabling audit trail reconstruction. Requirement 8.6 prohibits hard-coded credentials in scripts, configuration files, or application code, a requirement that is frequently violated in agent deployment patterns where API keys and payment credentials are embedded in system prompts or configuration files for convenience. PCI DSS 8.6.2 specifically requires that passwords and authentication credentials for application and system accounts not appear in source code, and the requirement has been interpreted by QSAs to include prompts stored in agent orchestration systems [25].
Requirement 7, covering access control based on business need and least privilege, applies directly to the scope of permissions granted to agent identities. The PCI SSC’s AI Principles guidance emphasizes that the least-privilege requirement is especially important for AI systems, because granting an agent access to more systems and data than necessary for its specific task amplifies the potential harm if the agent is compromised or misbehaves [26]. Organizations should implement agent permission scoping through credential separation: an agent authorized to process vendor invoice payments should hold credentials scoped only to the accounts payable payment system, not to general financial accounts, and should hold only the specific API permissions required for payment submission — not account management, configuration, or administrative functions.
Requirement 10, mandating comprehensive logging of all access to system components, requires that agent transaction activity be captured in an immutable audit log with sufficient detail to reconstruct the full action sequence for any transaction. For agentic systems, this means logging not only the transaction submission event but also the authorization token validation, the intent context record identifier, any human confirmation events, and the policy engine decision that cleared the transaction for execution. The session identifier that links all agent actions within a workflow must be included in every log entry, enabling reconstruction of the complete action chain for forensic investigation. Requirement 10.5.1 requires that log data be protected from modification, deletion, or destruction, which for agentic systems means ensuring that agent processes do not have write access to their own audit logs — an architectural requirement that must be enforced at the infrastructure level [27].
| PCI DSS Requirement | Agentic Relevance | Implementation Guidance |
|---|---|---|
| Req. 7 — Least Privilege | Agent credentials must be scoped to minimum necessary payment functions | Separate API keys per agent role; no shared credentials across agent types |
| Req. 8.2.2 — Unique Non-Human Identity | Each agent deployment must have a unique, attributable identifier | Use ephemeral agent IDs tied to authenticated session; never shared IDs |
| Req. 8.6 — No Hard-Coded Credentials | Payment credentials cannot be embedded in prompts or config files | Credentials injected at runtime from secrets manager; never stored in agent memory |
| Req. 10 — Logging | Full transaction chain including authorization events must be logged | Immutable append-only log; agent has no write access to its own audit trail |
| Req. 12.3 — Risk Assessment | AI deployment introduces new risks requiring documented assessment | Annual agentic risk assessment covering intent verification gaps and fraud vectors |
5.2 PSD2/PSD3 Strong Customer Authentication
The EU’s Payment Services Directive 2 established Strong Customer Authentication as a baseline requirement for electronic payments within the European Economic Area, mandating that payment authentication rely on at least two independent elements: something the payer knows, something the payer possesses, or something the payer is [3]. The provisional agreement on PSD3 and the Payment Services Regulation, reached in November 2025, strengthens and extends SCA requirements while allowing two authentication elements from the same category under certain conditions [4]. Neither PSD2 nor the forthcoming PSD3/PSR framework explicitly addresses the scenario where the payer is, in operational terms, an AI agent.
The regulatory status of agentic payment execution under PSD2 and PSD3 has been analyzed extensively by legal practitioners following publication of the provisional agreement. The prevailing interpretation, confirmed by Taylor Wessing’s February 2026 analysis, is that PSD2 is technologically neutral and applies to AI agents initiating payment transactions on a customer’s behalf [3]. This means that organizations enabling agents to initiate payments are likely providing regulated payment services and may need to be authorized as payment service providers, or to partner with an authorized PSP. The entity that enables the agent to initiate transactions — whether the agent developer, the deploying organization, or a payment platform — must conduct a careful licensing analysis in each jurisdiction of operation [28].
For organizations that have determined that their agentic payment activities fall within a regulated payment services category, the SCA challenge is primarily one of consent architecture. PSD2 permits SCA to be satisfied for a series of recurring transactions if the payer provides authenticated consent upfront that covers a defined set of transactions meeting specified criteria. This creates an authorized pathway for agentic execution: the human principal authenticates once to establish a consent scope — defining the counterparty universe, maximum transaction values, and time bounds — and the agent’s subsequent transactions within that scope are covered by the initial SCA event [3]. The critical compliance requirement is that the consent scope be genuinely bounded and technically enforced; an unlimited or poorly scoped consent that functions as a blanket authorization for all agent transactions would be inconsistent with the proportionality principles underlying PSD2’s SCA exemption framework.
The European Banking Authority has been mandated to issue Regulatory Technical Standards covering SCA implementation under the PSD3/PSR framework, with the EBA’s technical roadmap projecting full implementation by late 2026 [29]. Organizations deploying agents in payment workflows should monitor EBA consultation papers and draft RTS closely, as the standards are expected to address — for the first time — the specific authentication and consent mechanisms applicable to automated payment initiation systems. Until those standards are published, organizations should apply the most conservative interpretation of existing SCA requirements to agentic transactions, treating each series of agent-initiated payments as requiring documented, authenticated, scoped human consent rather than inferring that agent operation falls within existing exemptions.
The Digital Operational Resilience Act (DORA), which entered into application in January 2025, adds a layer of third-party risk management obligation for financial institutions deploying AI agents sourced from external vendors. Under DORA, PSPs must subject their ICT third-party service providers to appropriate due diligence and must ensure that contractual arrangements with AI vendors meet minimum requirements for operational resilience, incident notification, and audit rights [30]. An AI agent framework used to execute payments is an ICT service that falls within DORA’s scope, and organizations should ensure their vendor contracts include the specific provisions DORA requires before deploying these systems in production payment workflows.
5.3 KYC/AML Implications
Anti-money laundering and know-your-customer frameworks impose obligations that become structurally complex when the transaction-initiating entity is an AI agent rather than a natural person. The foundational question — who is the beneficial owner of a transaction initiated by an agent — must be resolved clearly before an organization can satisfy its Customer Due Diligence obligations under FATF Recommendation 10 and the Financial Crimes Enforcement Network’s beneficial ownership rules [5]. The agent itself is not a natural person and cannot be a beneficial owner in any regulatory sense; the beneficial owner is the human or corporate principal on whose behalf the agent acts, and that principal must be identifiable and subject to CDD in the same manner as any other transaction initiator.
The practical implication is that organizations must maintain, and be able to produce on demand, a complete mapping from agent identity to the authenticated human or corporate principal who authorized the agent’s transaction authority for each session. This mapping must be kept current throughout the agent’s operational lifetime and must be linked to the CDD records maintained for the authorizing principal. When an agent initiates a transaction, the AML monitoring system should evaluate the transaction not against a profile of the agent but against a profile of the underlying beneficial owner, because money laundering risk inheres in the human network, not in the software system [31].
Transaction monitoring for AML purposes must be reconfigured for agentic contexts along the same lines described in Section 4 for fraud detection. The velocity and timing patterns of agentic transaction activity will trigger rule-based AML monitoring systems that were calibrated for human behavior, generating a flood of false positives that overwhelms compliance teams and desensitizes them to genuine suspicious activity. Organizations should work with their AML system vendors to develop agent-aware monitoring profiles that evaluate deviation from established agent behavioral baselines rather than applying human-calibrated absolute thresholds. FATF guidance emphasizes that AML systems must be proportionate and risk-based; applying human-pattern rules wholesale to agent transaction streams is neither proportionate nor risk-based [32].
The FinCEN Notice of Proposed Rulemaking issued in June 2024 explicitly encourages innovation in AML compliance while mandating that institutions conduct AI-focused risk assessments [33]. For organizations deploying agents with transaction authority, a dedicated AML risk assessment should address: how the agent’s behavioral patterns might be exploited to layer illicit funds through the organization’s payment system; how the agent’s intent context and authorization records will be maintained as evidence of legitimate transaction purpose; and how the organization will identify and report suspicious activity when the agent is the apparent transaction initiator. The assessment should be reviewed annually and updated whenever the agent’s capabilities or operational scope change materially.
Sanctions screening presents a particular challenge for agentic operation. Standard sanctions screening workflows interpose a human review step when an automated match is flagged, but a high-velocity agent may initiate transactions faster than asynchronous human review can clear them. Organizations should implement synchronous, pre-execution sanctions screening for all agent-initiated payments, ensuring that no transaction is submitted to the payment network before a clean screening result has been obtained and recorded. The screening record should be linked to the transaction’s audit trail to document compliance at the time of execution, not merely during post-hoc review [34].
6. Liability Framework
The liability landscape for unauthorized agent-initiated transactions is evolving rapidly but has not yet reached the stability of settled law. The core structural question — whether liability flows primarily to the agent developer, the deploying organization, the payment platform, or some combination — is currently answered differently by different contractual regimes, jurisdictions, and regulatory frameworks, creating significant uncertainty for organizations attempting to manage their exposure. What is clear is that this uncertainty will not persist: as agentic payment systems scale, the volume of disputes will force liability questions into courts and regulatory proceedings, and the resulting precedents will reshape the risk profile of agent deployment [35].
Agent Developer Liability. The developer of an AI agent framework is increasingly exposed to product liability claims when a design defect in the agent causes unauthorized financial transactions. The EU’s Product Liability Directive, which member states must implement by December 2026, explicitly classifies software and AI as products and permits strict liability claims against producers when a product is defective — meaning that its security properties fall below what the general public is entitled to expect [6]. Under this framework, an agent framework that lacks basic intent verification controls, that allows credentials to be embedded in prompts, or that does not enforce authorization scope boundaries could be characterized as defective, exposing its developer to liability for downstream financial harm without requiring the claimant to prove negligence. U.S. state legislatures are advancing parallel AI liability bills, though federal preemption questions remain unsettled following the Trump administration’s January 2026 executive order on AI regulation [36].
Deploying Organization Liability. The organization that deploys an agent in a payment workflow bears the primary operational liability for its actions. This liability flows from multiple sources: the organization is the responsible party for PCI DSS compliance within its payment environment; it is the account holder for the payment credentials the agent uses; it is the data controller for the personal financial data the agent processes; and, under most payment network rules, it is the merchant or account holder against whom chargebacks and unauthorized transaction claims are filed. Deploying organizations cannot contractually shift this regulatory liability to agent vendors, though they can and should seek contractual indemnification for losses caused by vendor-side defects [37]. The CFPB’s enforcement position — that technological complexity does not excuse compliance failures — means that deploying organizations should treat agent-initiated unauthorized transactions as a direct organizational liability, not an externality to be managed solely through vendor contracts.
Payment Platform Provider Liability. Payment platforms, networks, and processors occupy an intermediate position: they provide the infrastructure through which agent-initiated transactions flow, and they may be subject to liability for failing to implement controls that would have detected or prevented unauthorized agent transactions. Under PSD2 and the forthcoming PSD3/PSR, the PSP is liable to the payer for unauthorized transactions unless it can demonstrate that the transaction was authorized by the payer and that the authorization was obtained through compliant SCA mechanisms [3]. When the “payer” is an agent operating on a human principal’s behalf, the PSP’s ability to demonstrate compliant authorization depends on the quality of the consent and authorization records that the deploying organization has established — another reason why robust intent context verification is a prerequisite for regulatory compliance, not merely a best practice.
Insurance and Risk Transfer. Cyber insurance and commercial crime insurance policies have historically provided limited coverage for losses arising from authorized push payment fraud — where the account holder authorized the payment but was deceived into doing so. Agent-initiated transactions that result from prompt injection or goal hijacking may fall into this gap: the agent was “authorized” to make payments, but the specific transaction was initiated through manipulation rather than genuine human intent [38]. Organizations should review their insurance coverage specifically for agentic payment scenarios and negotiate endorsements that explicitly cover authorized-but-compromised agent transactions. Some insurers are beginning to offer agentic AI coverage as a distinct endorsement, and the market is likely to mature as claim volumes increase.
Contractual Allocation Frameworks. In the absence of settled law, contracts are the primary tool for allocating liability among parties in an agentic payment chain. Effective contractual frameworks between deploying organizations and agent developers should address indemnification for losses caused by agent design defects with specificity — naming agentic behaviors such as unauthorized transaction initiation, credential exfiltration, and scope inflation rather than relying on generic “AI error” language. Contracts between deploying organizations and payment platforms should address how the platform will handle disputes arising from agent-initiated transactions, including the evidentiary standards the platform will apply when the account holder claims that an agent’s action exceeded its authorized scope. In supply chain and procurement contexts where agents from multiple organizations interact, multilateral liability agreements addressing inter-agent authorization failures are increasingly necessary and should be established before production deployment begins [39].
7. Transaction Monitoring and Anomaly Detection
Effective transaction monitoring for agentic payment systems requires instrumentation at three distinct levels simultaneously: the individual transaction level, the session level, and the aggregate behavioral level across all agent instances operating within the organization. Each level captures different categories of risk and generates different types of signals; a monitoring architecture that operates at only one or two levels will systematically miss important threat patterns.
At the individual transaction level, monitoring should evaluate each pending payment against a set of real-time risk signals before execution. These signals include: whether the counterparty account has been used in recent agent sessions (counterparty novelty); whether the transaction amount falls within the statistical distribution of amounts in the agent’s behavioral profile (amount deviation); whether the transaction is being submitted outside the agent’s established operational hours without a documented schedule exception (temporal anomaly); and whether the stated payment purpose matches the agent’s current authorized task scope (purpose alignment). Transactions that trigger anomaly signals above a configured threshold should be held for human review rather than automatically rejected, since high-risk signals may reflect legitimate but unusual business activity rather than compromise [40].
At the session level, monitoring should track cumulative metrics that evolve throughout an agent’s operational period. Cumulative authorized spend relative to the session ceiling provides an early warning of scope creep before the ceiling is breached. The rate at which new counterparties are being added to the agent’s payment history — counterparty acquisition velocity — provides a sensitive indicator for beneficiary creep attacks that introduce fraudulent recipients gradually. The variance in payment amounts over the session compared to the agent’s historical variance may indicate that the agent is operating under a manipulated instruction set that differs from its established profile. Session-level alerts should be generated when any of these metrics deviate beyond configured thresholds, and a human security analyst should be notified for review before the session continues [41].
At the aggregate level, monitoring should track the behavior of all agent instances collectively to detect coordinated attacks, systemic misconfiguration, or platform-level compromise that would not be visible from any individual agent’s session. If multiple agents suddenly begin initiating payments to previously unknown counterparties in the same geographic region, or if aggregate payment velocity across all agents spikes simultaneously without a corresponding business event, these patterns may indicate that a shared component — the orchestration framework, a shared tool, or a common prompt injection vector — has been compromised. Aggregate monitoring requires maintaining organization-level baselines in addition to per-agent baselines, and it requires that each agent’s monitoring data be written to a shared analytics environment that can support cross-agent correlation queries [42].
Several specific detection rules address the agentic misbehavior patterns most commonly observed in security research and early production incidents. Unusual timing detection identifies agent payment activity that falls outside established operating windows, particularly during periods of reduced human oversight such as holidays, executive travel periods, or end-of-quarter close periods when operational staff attention is focused elsewhere. Scope creep detection monitors for systematic growth in the number of unique counterparties contacted, the geographic spread of payment destinations, and the average transaction size over rolling time windows. Beneficiary anomaly detection flags payments to accounts that share structural characteristics with known fraudulent accounts — unusual name formatting, newly registered bank accounts, accounts in high-risk jurisdictions — even when those accounts have not yet appeared in sanction or fraud watchlists [43].
8. Rollback and Dispute Resolution
The operational reality of agentic payment systems includes the near certainty that, at some scale of deployment, transactions will be initiated that should not have been — whether through agent error, system malfunction, adversarial manipulation, or genuine unauthorized access. The ability to identify these transactions quickly, preserve the evidence needed to investigate and dispute them, and execute reversals where technically possible are operational capabilities that organizations must build before deploying agents with transaction authority, not after the first incident [44].
Technical rollback capabilities are constrained by the settlement characteristics of different payment instruments. Credit card transactions can be reversed through chargeback procedures within defined time windows, subject to the payment network’s dispute rules. ACH transactions can be returned within defined deadlines that vary by transaction type under NACHA rules. Wire transfers and real-time payment transactions may be effectively irrevocable once settled, though fraud recall processes exist in some networks that can recover funds if the receiving institution’s cooperation is obtained quickly. Organizations deploying agents must understand the reversibility characteristics of each payment instrument the agent is authorized to use and must calibrate their detection and response timelines accordingly — a missed four-hour ACH return window or a two-day wire recall window can mean the difference between full fund recovery and total loss [45].
Evidence preservation begins before a dispute is filed and must be designed into the agent’s audit infrastructure from the outset. The complete session log for any transaction that becomes the subject of a dispute — encompassing the intent record, all tool invocations, all intermediate reasoning steps if the system captures them, the authorization token validation event, the policy engine decision, and the transaction submission and network response — constitutes the primary evidence of whether the transaction was properly authorized. This log must be retained in immutable form for a period that exceeds the applicable regulatory retention requirement and the relevant statute of limitations for financial fraud claims. Logs must be maintained in a format that can be produced to payment networks, regulators, and courts in a readable and verifiable form, including documentation of the log’s chain of custody and integrity verification [46].
The dispute escalation path for an agent-initiated transaction that the human principal disputes as unauthorized follows a specific sequence. The principal notifies the deploying organization through an established channel, triggering immediate suspension of the relevant agent’s transaction authority while investigation proceeds. The deploying organization’s security team extracts the complete session record and conducts a preliminary investigation to determine whether the transaction resulted from authorized agent operation, agent error, or adversarial compromise. If adversarial compromise is identified, the incident response process expands to include the agent developer and, where applicable, the orchestration platform provider, each of whom must preserve their own records in parallel. If the investigation supports a claim of unauthorized transaction, the deploying organization submits a dispute to the payment network with the supporting evidence from the agent’s audit trail, asserting that the transaction exceeded the agent’s authorized scope [47].
Human escalation protocols must be defined in advance and rehearsed through tabletop exercises. Key questions that escalation protocols should address include: who has authority to suspend an agent’s transaction credentials during an active incident; what is the notification sequence for informing the human principal, the agent developer, the payment platform, and regulators; at what threshold does an incident trigger mandatory regulatory reporting under applicable breach notification laws; and how are customers or counterparties notified if their payment data was exposed during the incident. Organizations that rehearse these protocols before their first incident will respond materially faster and more effectively than those that construct the response plan under pressure during an active investigation [48].
9. Framework Alignment
The following table maps ATSF components to their corresponding requirements in PCI DSS v4.0.1, the PSD2/PSD3 regulatory framework, the OWASP Top 10 for Agentic Applications 2026, and the CSA AI Controls Matrix domains. This alignment enables organizations to map existing compliance programs to ATSF adoption and to prioritize implementation based on their current regulatory obligations.
| ATSF Component | PCI DSS v4.0.1 | PSD2/PSD3 | OWASP ASI 2026 | AICM Domain |
|---|---|---|---|---|
| Intent Context Verification — signed intent records | Req. 8.2.2, Req. 12.3 | Article 64 SCA consent scope | ASI01 (Agent Goal Hijack), ASI09 (Human-Agent Trust Exploitation) | Identity and Access Management (IAM), Application and Interface Security (AIS) |
| Intent Context Verification — out-of-band confirmation | Req. 8.6, Req. 10.2 | SCA independent channel requirement | ASI01, ASI03 (Identity and Privilege Abuse) | IAM, Human Oversight and Accountability (HOA) |
| Multi-Party Authorization — tiered thresholds | Req. 7.2, Req. 8.2.2 | SCA recurring transaction consent | ASI07 (Insecure Inter-Agent Communication) | IAM, Governance Risk and Compliance (GRC) |
| Multi-Party Authorization — smart authorization workflow | Req. 7.2.4, Req. 10.7 | PSR fraud liability provisions | ASI03, ASI08 (Cascading Failures) | IAM, Logging and Monitoring (LMO) |
| Fraud Detection — agent behavioral baselines | Req. 10.3, Req. 12.3 | PSD3 TRA risk assessment requirements | ASI06 (Memory and Context Poisoning) | LMO, Model Security (MOD) |
| Fraud Detection — velocity controls | Req. 10.6.1 | PSD3/PSR fraud detection mandates | ASI01, ASI08 | LMO, Incident Response (IR) |
| Fraud Detection — counterparty novelty scoring | Req. 10.2, Req. 12.3 | PSD3 payer authentication obligations | ASI02 (Tool Misuse and Exploitation) | LMO, Data Security and Privacy (DSP) |
| PCI DSS Compliance — agent identity | Req. 8.2.2, Req. 8.6 | N/A (payment network scope) | ASI03 | IAM |
| PCI DSS Compliance — audit logging | Req. 10.2, Req. 10.3, Req. 10.5 | DORA audit trail requirements | ASI05 (Inadequate Deployment Safeguards) | LMO, Audit and Assurance (AA) |
| SCA Compliance — consent architecture | N/A (EU regulatory scope) | Art. 4(30) PSD2, PSR SCA provisions | ASI09 | IAM, HOA |
| KYC/AML — beneficial owner mapping | Req. 12.3 | PSD3 customer due diligence | ASI03, ASI10 (Rogue Agents) | IAM, GRC |
| KYC/AML — AML behavioral monitoring | Req. 10.6, Req. 12.3 | AMLD6 transaction monitoring | ASI01, ASI06 | LMO, MOD |
| Liability — contractual allocation | Req. 12.8 (third-party management) | PSP liability provisions PSD3/PSR | ASI04 (Supply Chain Vulnerabilities) | GRC, Incident Response |
| Liability — evidence preservation | Req. 10.5.1, Req. 12.10.7 | DORA incident records | ASI05 | LMO, AA |
| Transaction Monitoring — session-level metrics | Req. 10.6.1, Req. 10.7 | PSD3 fraud monitoring mandates | ASI08, ASI10 | LMO |
| Rollback — audit trail for disputes | Req. 10.2, Req. 10.5 | PSR chargeback and dispute rights | ASI05 | AA, IR |
| Rollback — escalation protocols | Req. 12.10, Req. 12.10.6 | PSP unauthorized transaction reporting | ASI09, ASI10 | IR, HOA |
References
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