Trusted Agent Protocol: What Akamai–Visa Enables

Payment security doesn’t usually fail in dramatic ways. It fails in the boring ways: a subtle spoofed domain, a bot swarm that looks “almost human,” a legitimate customer forced into step-up authentication one too many times, a merchant losing conversion during peak season.

That’s why the news that Akamai has integrated with Visa’s Trusted Agent Protocol (TAP) matters—even if the headline sounds like plumbing. This is infrastructure, and infrastructure is where fraud gets prevented (or allowed) at scale. If you’re building or operating payment flows—issuer, acquirer, PSP, merchant, gateway—trusted protocols and interoperable integrations are becoming the prerequisite for effective AI-driven fraud detection and routing.

This post is part of our “AI in Payments & Fintech Infrastructure” series, and I’ll take a clear stance: AI can’t compensate for weak identity signals and fragmented network trust. TAP-like approaches improve the signal quality. Better signals make AI models more accurate, less noisy, and easier to operationalize.

What Visa’s Trusted Agent Protocol (TAP) actually changes

Answer first: TAP is about making “who is calling whom” more trustworthy across payment interactions, so systems can make safer decisions with fewer false positives.

Most payment risk stacks are great at scoring a transaction after it’s assembled. The weak spot is earlier: the integrity of the parties and channels involved in the request—the app, the domain, the API client, the edge network, the device, the session.

TAP fits into a broader industry shift: payments are moving from isolated checks (each participant verifying independently) to shared, standardized trust frameworks. When multiple parties can rely on a common way to represent “this request is coming from a verified agent,” you can:

• Reduce duplicate verification work across intermediaries
• Make step-up authentication more targeted
• Improve payment routing decisions (send cleaner traffic down the fastest path)
• Detect coordinated fraud earlier (before it hits authorization)

Why interoperability is the whole point

A protocol only helps if it’s used across the chain. That’s why integrations matter.

If a merchant uses one security provider, a PSP uses another, and an issuer has its own stack, you often end up with inconsistent trust signals. The result is predictable:

• Good customers look suspicious in one leg of the journey
• Fraudsters find the “lowest-confidence” hop and exploit it
• Your AI models get trained on messy labels and conflicting inputs

TAP aims to standardize how trusted agent context can be exchanged so each participant can make decisions with shared context rather than isolated guesses.

Why Akamai’s role matters in payment infrastructure

Answer first: Akamai sits where a lot of the highest-value signals live: at the edge, in front of web apps, APIs, and high-volume transaction surfaces.

Akamai is widely used for application delivery and security—especially for high-traffic consumer experiences and API-heavy platforms. That placement is strategic for payments because many modern payment journeys start before checkout:

• Account login
• Credential storage and token provisioning
• Session creation and device binding
• Cart and pricing integrity
• Payment method selection

Fraud often begins there. By the time you see the authorization request, the attacker may already have:

• Taken over an account
• Tested stolen credentials (“carding”)
• Established a “warm” session that looks legitimate

TAP + edge security creates earlier, cleaner risk signals

When edge infrastructure can participate in a trusted protocol, you get a stronger foundation for downstream decisions:

• Earlier detection: identify automated abuse and anomalous patterns before authorization
• Higher-confidence identity context: fewer blind spots between “web security” and “payment risk”
• Better continuity: the session that started at login can be tied more reliably to the payment action

That continuity is what AI systems crave. If you want models that actually hold up in production, you need consistent identifiers and trustworthy event trails.

How AI can use TAP for smarter fraud detection and routing

Answer first: TAP improves the “input layer” for AI—stronger provenance signals, cleaner features, and more stable decisioning across participants.

AI fraud detection in payments tends to fail in two predictable ways:

1. Noisy signals: the model gets contradictory or low-confidence inputs, so it overfits to proxies (like geography) and spikes false positives.
2. Broken feedback loops: labels arrive late, chargebacks are sparse, and the model can’t learn fast enough against new attack tooling.

A trusted-agent protocol doesn’t magically solve those problems, but it helps in the most practical way: it increases the reliability of who/what is initiating requests.

Three AI improvements you can expect from trusted protocols

1. Feature quality improves
   • More dependable “source-of-request” attributes
   • Better separation between human sessions and automated traffic
   • Fewer “unknown” buckets that dilute model usefulness

1. Lower false positives through context Many teams try to reduce fraud by tightening thresholds. That typically harms conversion.

   A better approach is to add context that lets the model be strict on bad traffic and lenient on good traffic.

2. Smarter transaction routing Routing isn’t just cost and latency anymore. It’s also risk-aware path selection.

   If your orchestration layer can trust the upstream context more, you can:

   • Route “clean” traffic to the fastest rails
   • Route ambiguous traffic through flows with stronger authentication
   • Apply step-up selectively rather than globally

A practical rule: routing decisions should be risk decisions, not just commercial decisions.

A concrete scenario (holiday traffic, real constraints)

December is when all of this gets stress-tested. Traffic spikes, bots spike, and fraud spikes.

Imagine a mid-market retailer running:

• A high-volume web storefront
• A mobile app
• Multiple PSP connections for redundancy

Without shared trust signals, the retailer often responds by:

• Increasing 3DS step-up
• Tightening velocity rules
• Blocking broader IP ranges

Conversion drops, and customer support gets flooded.

With stronger trusted-agent context (and edge visibility feeding into it), the risk engine can:

• Identify bot-driven card testing earlier
• Allow known-good sessions to pass without extra friction
• Trigger step-up only for sessions with weak provenance

That’s not theoretical—it’s the difference between “security” and operationally sustainable security.

What payment teams should do next (even if you’re not on TAP yet)

Answer first: Treat trusted protocols as a roadmap item, and start by fixing identity continuity and signal governance across your stack.

Most teams approach AI fraud detection by shopping for a model. I’ve found the bigger win usually comes from making your signals usable.

1) Map your trust gaps across the payment journey

Create a simple flow map from:

• Login → session creation → checkout → payment authorization → post-auth events

Then mark where you lose continuity:

• Different identifiers across web/app/PSP
• No stable session ID passed to payment risk
• Edge security events not available to fraud tooling

Your goal: one coherent narrative of the customer session.

2) Standardize event collection and decision logging

If you want AI models you can defend to leadership (and regulators), logging matters.

Minimum viable logging:

• Decision outcome (approve/decline/step-up)
• Features used (or feature set version)
• Reason codes (human-readable)
• Downstream outcomes (chargeback, refund, customer complaint)

This is how you build a fraud program that improves month over month.

3) Add risk-aware routing rules before you “go full AI”

Not every team needs end-to-end autonomous decisioning. A strong middle ground:

• Rules route transactions based on risk bands
• AI assigns risk bands using stable, trusted signals

It’s controllable, explainable, and usually deploys faster.

4) Pressure-test your bot and ATO defenses

If your fraud losses are rising but auth decline rates are also rising, you likely have:

• Bot activity inflating noise
• Account takeover (ATO) upstream of payments

Run drills:

• Credential stuffing simulation
• Card testing simulation
• Promo abuse simulation

Your AI will only be as good as the environment it’s deployed into.

People also ask: TAP, trusted agents, and payments AI

Is TAP the same as 3DS or tokenization?

No. 3DS is an authentication framework, and tokenization is about protecting PAN data. TAP is closer to a trust and provenance layer: who is acting, through what channel, and whether that agent is recognized.

Does a trusted protocol eliminate fraud?

No. It reduces ambiguity. Fraud adapts. The win is that your controls can be more precise, which usually means fewer false positives and better customer experience.

Where does this fit in a modern fintech stack?

Right at the infrastructure intersection: edge security, API gateways, orchestration, risk engines, and network-level protocols. If you’re building AI in payments seriously, these layers need to work together.

Where this goes next for AI in payments infrastructure

Trusted protocols like Visa TAP are a signal that the industry is done pretending each company can solve trust alone. The more interoperable the infrastructure becomes, the more practical it is to apply AI safely—because the model isn’t guessing in the dark.

If you’re leading payments, risk, or platform engineering in 2026 planning cycles, here’s the stance I’d take into budget discussions: invest in shared trust signals and integration maturity before you invest in more model complexity. Most companies get that order wrong.

If your team wants to reduce fraud without punishing good customers, the next step is straightforward: audit your trust gaps, improve identity continuity, and treat trusted protocols as a strategic dependency—not a “nice-to-have integration.”

What would change in your approval rates and step-up volume if your upstream session context was actually trustworthy across every hop?