DEV Community: Vaibhav Shakya

When an AI Agent Becomes Over-Privileged

Vaibhav Shakya — Thu, 24 Sep 2026 04:50:00 +0000

The real danger in an AI agent is not simply that it can make a wrong decision.

It is that the wrong decision may already have permission to become a production action.

As agents move from answering questions to issuing refunds, changing configuration, rotating credentials, triggering deployments, or calling other agents, authorization becomes part of the architecture itself.

The key design principle is:

Reasoning authority and execution authority should remain separate.

An agent may conclude that a refund, credential rotation, configuration update, or deployment is necessary.

That does not mean it should automatically have authority to perform that action.

The Permission Composition Problem

Agent permissions often grow gradually.

A support agent may begin with:

transaction read access,
customer read access,
permission to create investigation notes.

Later, refund capability is added.

Then account-state changes.

Then merchant configuration.

Eventually the same agent can perform:

investigate
   ↓
decide
   ↓
refund
   ↓
change account state
   ↓
modify configuration

Each permission may look reasonable individually.

The risk appears when an autonomous planner can combine them.

This is why agent security is not only an API-key or IAM problem. It is also a capability-composition problem.

Prompts Are Not Authorization

A prompt such as:

Never modify permissions unless explicitly requested.

may influence behavior.

It is not an authorization boundary.

If the agent already holds credentials capable of performing the operation, the system is depending on model behavior for security.

A stronger pattern looks like this:

Agent proposes action
        ↓
Authorization layer evaluates
        ↓
Allow / Deny / Require Approval
        ↓
Scoped execution authority
        ↓
Tool executes

The model decides what it wants to do.

An independent enforcement layer decides what it is actually allowed to do.

Scope Authority to the Task

Suppose an agent is investigating transaction TX-19281.

Instead of giving broad permanent permissions such as:

transactions:read
refunds:create

the architecture should, where practical, scope authority closer to:

action: transactions.read
resource: TX-19281
workflow: dispute-7813
expires: shortly

If the workflow later requires a refund, that should trigger another authorization decision.

A successful read should not automatically create write or financial authority.

Useful authorization dimensions include:

principal,
initiating user,
requested action,
target resource,
workflow purpose,
duration,
risk level.

Treat Authority-Changing Tools Differently

Not every write operation has the same impact.

Consider:

updateTicket()
sendEmail()
issueRefund()
addRole()
createApiToken()

These should not all live behind the same generic "write" permission.

addRole() and createApiToken() can change future authority.

That makes them fundamentally different from ordinary business operations.

IAM changes, credential creation, policy changes, security configuration, and agent delegation deserve stronger boundaries.

Separate Planning From Execution

Agents should be free to reason about high-risk actions without automatically receiving permission to execute them.

For example:

Planner
   ↓
Proposed Action Graph
   ↓
Policy Evaluation
   ↓
Approved Action Graph
   ↓
Executor

This also improves auditability.

The system can record:

what the agent proposed,
what policy allowed,
what required approval,
what was denied,
what actually executed.

That difference matters during incident analysis.

Watch Agent-to-Agent Delegation

Multi-agent systems add another risk.

Customer Agent
      ↓
Payment Agent
      ↓
Operations Agent

If calling a more privileged agent effectively gives the caller access to everything the downstream agent can do, privilege can propagate through the agent graph.

Delegation should preserve context such as:

who started the workflow,
which agent delegated it,
which action is requested,
which resources are in scope,
whether further delegation is allowed.

Calling another agent should not automatically mean inheriting its authority.

A Practical Example

Consider an operations agent handling failed merchant settlements.

A naive implementation gives it:

ledger.read
merchant.read
settlement.retry
merchant.update
credentials.create

The agent decides that invalid credentials caused the failure.

It creates new credentials, updates the merchant, and retries settlement.

The workflow may succeed technically.

But the model was allowed to rotate security credentials because its own reasoning decided that rotation was necessary.

A stronger architecture lets the agent investigate and propose:

ROTATE_CREDENTIAL
merchant = M1029
reason = authentication_failure

The authorization layer identifies credential rotation as a sensitive operation.

A stronger policy or human approval is applied.

Where supported, execution authority is scoped to that specific merchant and operation, used once, and then expires.

The agent remains autonomous enough to investigate and recommend actions without permanently holding open-ended administrative authority.

The Architectural Goal

AI agents will become more autonomous.

They will interact with more systems, execute longer workflows, and delegate more work.

Their standing authority does not need to expand at the same rate.

An agent can understand how to deploy production software without always possessing production deployment rights.

It can identify an IAM issue without being able to modify IAM.

It can recommend a refund without automatically being able to issue one.

The key question is not only:

Can the agent make the right decision?

It is also:

What happens when it makes the wrong one?

Good agent architecture ensures that a bad decision remains constrained by independent authorization boundaries.

Read the full article on Medium

Designing API Abuse Protection for Mobile Apps: Replay, Bots, Emulators, Velocity, and Rate Limits

Vaibhav Shakya — Wed, 23 Sep 2026 04:19:25 +0000

Designing API Abuse Protection for Mobile Apps

Authentication is necessary, but it does not mean every authenticated action is legitimate.

A valid session can still automate OTP requests, replay sensitive operations, or distribute abuse across accounts and devices.

That is why API abuse protection needs multiple layers.

Protect the Action, Not Just the Endpoint

A useful model is:

Mobile Client
    ↓
Request Integrity
    ↓
Authentication
    ↓
Replay Protection
    ↓
Velocity + Rate Controls
    ↓
Business Risk
    ↓
Authorization
    ↓
Business Service

Each layer solves a different problem.

Authentication validates identity or session evidence. Authorization determines whether the actor can perform the operation.

Replay protection helps reject reused request material, while rate limits control resource consumption and velocity checks evaluate suspicious behavior across users, devices, networks, and time windows.

Replay and Retries Are Different

For sensitive operations:

timestamp
→ bounded freshness

nonce
→ replay resistance

idempotency
→ controlled legitimate retries

These controls are related, but they are not interchangeable.

A retry caused by a network timeout should not automatically execute the same financial operation twice.

Integrity Is a Signal

Mobile integrity mechanisms can provide evidence about the application or device environment.

That evidence should be validated by the backend and combined with other signals.

integrity
+ session
+ velocity
+ business context
+ authorization
→ server-side decision

A strong integrity signal does not prove an action is legitimate, and a weak signal does not independently prove fraud.

Rate Limiting Is Not Abuse Detection

A rule such as:

10 OTP requests / minute / IP

can help, but attackers can distribute activity.

A broader model may consider:

phone number
account
device/install
IP or network range
beneficiary or target
recent behavior

Rate limiting constrains usage.

Velocity analysis looks for patterns.

Device Signals Are Not Proof

An emulator or unusual environment can correlate with automation, but it does not automatically mean abuse.

Attackers can also automate physical devices.

Environment signals should therefore remain part of a wider risk decision.

Design for Failure

Abuse-control infrastructure can fail too.

If a velocity store becomes unavailable, allowing everything may remove an important control, while blocking everything may create an availability incident.

The degraded behavior should depend on the operation risk and remaining protections.

Residual Risk Remains

No integrity check, rate limiter, device signal, or behavioral rule eliminates API abuse.

The practical goal is layered protection:

increase the cost of abuse
restrict suspicious behavior proportionally
preserve legitimate usage
retain enough telemetry to explain decisions

The key architectural question is:

Does this action make sense for this identity, device, request, behavior, and business context?

That is stronger than simply asking whether the endpoint is authenticated.

Read the full article on Medium:

https://medium.com/@vaibhav.shakya786/designing-api-abuse-protection-for-mobile-apps-replay-bots-emulators-velocity-and-rate-limits-fcba502e5eeb

Not Every Login Deserves the Same Trust: Architecting Adaptive Authentication for Financial Apps

Vaibhav Shakya — Tue, 22 Sep 2026 04:23:59 +0000

A successful login establishes authentication.

It should not automatically mean that every action in that session deserves the same level of trust.

Consider two users:

One logs in from a familiar device and checks transaction history.
Another logs in after account recovery, from a new device, and immediately attempts to change the registered mobile number or add a beneficiary.

Both may have successfully authenticated.

But the required assurance for their next actions can be very different.

Authentication Is Only One Layer

A useful architecture separates:

Identity
   ↓
Authentication
   ↓
Session Assurance
   ↓
Action Authorization

The risk layer can evaluate contextual evidence such as:

device history
app-integrity signals
network reputation
recent account recovery
authentication method
behavioral anomalies
requested operation
transaction context

But the risk engine should provide evidence, not become the final security authority.

The policy layer should decide whether the requested operation is:

text ALLOW STEP_UP RESTRICT DENY

Keep Sensitive Decisions on the Backend

The mobile application can collect security signals, but sensitive authorization decisions should remain on trusted backend infrastructure.

For request-specific security evidence, the backend should also validate:

freshness
request or challenge binding
server-side account state
current session assurance

Client-side checks can improve UX, but they should not become the final authorization boundary.

Step-Up Should Actually Increase Assurance

An additional challenge is useful only when it meaningfully strengthens confidence.

Repeating the same potentially compromised credential may add little protection.

Depending on the threat model, stronger verification may involve cryptographic authenticators, device-bound credentials, or transaction-specific confirmation.

The objective is not more friction.

It is sufficient assurance for the action being attempted.

Trust Can Change During a Session

A session can begin normally:

text 09:00 Login 09:02 View balance 09:05 Change email 09:07 Add beneficiary 09:08 Initiate large transfer

The security context at 09:08 is no longer necessarily the same as it was at 09:00.

Sensitive account changes can reduce the assurance the system is willing to rely on.

This is why adaptive authentication works better when trust is treated as mutable state rather than a permanent login property.

Authentication Is Not Enough for Financial Execution

Sensitive financial operations also need protection against:

replay
duplicate retries
stale authorization
concurrent requests
invalid workflow transitions
partial dependency failures

Authentication and authorization therefore need to work alongside transaction-state validation, idempotency, and atomic backend execution.

The Core Principle

The better question is not only:

Did this person log in successfully?

It is:

How much confidence do we have in this session right now, and is that confidence sufficient for what the user is about to do?

Adaptive authentication is not about challenging users more often.

It is about applying the right level of assurance to the right action at the right time.

Read the full article on Medium:

https://medium.com/@vaibhav.shakya786/not-every-login-deserves-the-same-trust-architecting-adaptive-authentication-for-financial-apps-13a28ecc4caf

When the Model Is Wrong but the System Still Works: Architecting Model-Risk Controls for Financial AI

Vaibhav Shakya — Mon, 21 Sep 2026 04:12:44 +0000

When the Model Is Wrong but the System Still Works

The safest financial AI architecture is not necessarily the one with the most accurate model.

It is the one designed so that a wrong model decision does not automatically become a wrong financial action.

A model may misunderstand intent, select the wrong transaction, infer the wrong state, or generate a perfectly valid tool request for the wrong action.

The key architectural boundary is:

Model → proposed intent → deterministic policy → execution

The model interprets the request.

The platform still needs to independently enforce:

authenticated identity
resource authorization
authoritative transaction state
limits and policy rules
stronger authentication where required
explicit confirmation
concurrency protection
retry and idempotency semantics

A confirmation on mobile is useful evidence of user intent, but it should not replace backend authorization.

The same applies to model confidence and human review. A highly confident model can still be wrong, and a human approval should not bypass transaction-state checks, authorization, limits, or idempotency.

Model upgrades should also be treated as behavioral production changes. Monitor policy rejections, tool usage, overrides, duplicate attempts, and other workflow-level signals — not only latency and HTTP errors.

The more useful question is not only:

“How accurate is the model?”

It is:

“What happens when the model is wrong?”

A robust financial AI system assumes that failure will eventually happen and limits what that failure can change.

Read the full article

https://medium.com/@vaibhav.shakya786/when-the-model-is-wrong-but-the-system-still-works-architecting-model-risk-controls-for-financial-f73d9dcf2581

UPI Is Still Free for Users - So What Does India's New 0.4% Merchant Charge Actually Change?

Vaibhav Shakya — Fri, 18 Sep 2026 04:18:22 +0000

UPI users are not being charged 0.4%.

From October 15, the new MDR framework applies mainly to qualifying merchant payments above ₹2,000. P2P transfers remain free, lower-value merchant payments stay outside the standard MDR, and eligible small merchants continue under zero-MDR treatment.

The bigger change is on the merchant side.

For qualifying transactions, MDR creates a direct payment-processing cost. The idea is to support the economics of running large-scale payment infrastructure — including reliability, fraud controls, security, reconciliation, and support.

But this also creates a trade-off.

Even when consumers are not directly charged, merchant payment costs can still influence margins, discounts, pricing, or preferred payment methods over time.

So the real question is not simply:

Should UPI be free or paid?

It is:

Who should ultimately bear the cost of running and improving such a large digital-payment ecosystem?

Read the full article on Medium:

https://medium.com/@vaibhav.shakya786/upi-is-still-free-for-users-so-what-does-indias-new-0-4-merchant-charge-actually-change-8f7d15a0eee4

From Prompt to Payment: How Prompt Injection Becomes a Financial Security Incident

Vaibhav Shakya — Thu, 17 Sep 2026 04:41:09 +0000

Prompt injection becomes a financial security problem when manipulated AI output can influence a privileged transaction path.

An AI assistant may understand:

"Refund this transaction."

It may retrieve the correct payment and even generate a valid tool call.

But that tool call should remain a proposal — not authorization.

Keep authorization outside the LLM

Sensitive financial actions should still pass through deterministic backend controls that validate:

Authenticated identity
Transaction ownership
Current transaction state
Limits and policy rules
Step-up authentication or approval
Idempotency and duplicate execution protection

The model can interpret intent and prepare an action, but it should not decide whether money is actually allowed to move.

Indirect Prompt Injection

The malicious instruction may also come from:

Uploaded documents
Emails
Support tickets
Merchant content
Retrieved knowledge

Even if that content successfully influences the model, the surrounding platform should still reject unauthorized financial actions.

Smaller Tools, Smaller Blast Radius

Prefer narrow tools such as:

get_transaction()

prepare_refund()

request_refund()

instead of giving the model broad internal API or execution access.

The Architectural Principle

Prompt injection detection is useful, but it should not be the only security boundary.

Assume the model can be manipulated. Design the financial path so model compromise does not automatically become authorization compromise.

👉 Read the full article on Medium:

https://medium.com/@vaibhav.shakya786/from-prompt-to-payment-how-prompt-injection-becomes-a-financial-security-incident-13380b3d9c19

From Agentic AI Risks to Banking Controls: An Architect’s Security Mapping for Financial AI

Vaibhav Shakya — Wed, 16 Sep 2026 04:38:44 +0000

Agentic AI Security in Financial Systems

Once an AI assistant can query financial data, invoke tools or propose state-changing operations, securing the prompt is only one part of the architecture.

The important boundary is:

Model → proposed action → deterministic policy → controlled tool → financial service

The model may interpret intent and construct a plan, but identity, authorization, financial limits and consequential execution should remain independently enforced.

That becomes especially important for risks such as goal hijacking, tool misuse, excessive privilege, poisoned memory, unsafe inter-agent communication and cascading retries.

Financial operations also need conventional distributed-systems protections. Stable operation identifiers, durable workflow state and appropriate idempotency help keep ambiguous retries from becoming repeated financial execution.

The core principle:

Do not try to make the model the financial trust boundary. Limit what the surrounding system permits it to do.

Read the full article on Medium: https://medium.com/@vaibhav.shakya786/from-agentic-ai-risks-to-banking-controls-an-architects-security-mapping-for-financial-ai-a0b048c04a71

Designing AI Support Assistants for FinTech Apps with Human Escalation

Vaibhav Shakya — Tue, 15 Sep 2026 04:46:13 +0000

Designing Safer AI Support Workflows in FinTech

Adding an AI assistant to a financial application changes architecture once the conversation moves from:

“Why did my payment fail?”

to requests such as refunds, payout-account changes, blocked-account actions, or settlement disputes.

The key boundary is:

The model interprets. Deterministic systems authorize and execute.

The assistant can classify intent, collect references, retrieve narrowly scoped information, and prepare an escalation. But authentication, ownership checks, transaction-state validation, financial limits, policy decisions, and privileged execution should remain outside the model.

Tool design matters as much as prompting. An assistant should receive only the capabilities required for its workflow, with server-side authorization and parameter validation around every sensitive operation.

Retrieval needs the same treatment. Data should be scoped to the authenticated user, tenant, merchant, and visibility policy before it reaches the model rather than retrieving broadly and asking the model not to expose unauthorized results.

Human escalation is also more than a fallback for low model confidence. A request may be understood correctly and still require escalation because of financial impact, security risk, policy requirements, repeated failure, or explicit customer choice.

And the surrounding system still needs conventional distributed-system engineering: idempotent operations, bounded retries, timeouts, audit events, explicit state transitions, and structured escalation context.

The objective is not to give AI every capability available to a support agent.

It is to define exactly where AI interpretation ends and controlled authority begins.

Read the full article: https://medium.com/@vaibhav.shakya786/designing-ai-support-assistants-for-fintech-apps-with-human-escalation-2467767571d0

Android WebView: The Attack Surface Most Teams Ignore

Vaibhav Shakya — Fri, 11 Sep 2026 04:41:05 +0000

A WebView does more than display HTML. It can connect web content to application identity, cookies, deep links, device permissions, native code, and backend APIs.

That makes it a trust boundary, not merely a UI component.

Where the risk begins

The native application chooses the initial URL, but the loaded page may:

Redirect to another origin
Load third-party scripts or frames
Send messages to native code
Request camera, microphone, location, or file access
Trigger authenticated backend operations

HTTPS protects data in transit when certificate validation succeeds, but it does not protect against compromised scripts, unsafe native bridges, malicious redirects, stolen sessions, or broken backend authorization.

Applications should parse URLs and validate the exact HTTPS origin. Unapproved destinations should open outside the privileged WebView, while unknown or dangerous schemes should be rejected.

Native bridges represent authority

A method exposed through addJavascriptInterface is not simply a helper. It gives web content access to a native capability.

Classic JavaScript interfaces expose their object to every frame without reliably identifying which frame initiated the call. Origin-scoped messaging provides stronger control, but a compromised script on an approved origin can still use the capabilities granted to that origin.

A safer bridge should:

Accept only a small, versioned command set
Verify the source origin and frame
Validate message structure and payload size
Handle duplicate or replayed messages
Request native actions instead of executing privileged operations directly
Use a safe fallback when the required messaging feature is unavailable

For example, a page may send an orderId to request a native payment review. It should not be allowed to supply an authoritative amount or complete the payment directly.

The backend remains authoritative

The WebView and native application are both clients. Neither should be treated as the final authorization boundary.

For sensitive operations, the backend must independently validate:

User identity and authorization
Resource ownership
Canonical amounts
Current transaction state
Request freshness
Idempotency
Concurrent or repeated requests
Relevant fraud and risk signals

A signed or opaque identifier can improve integrity, but it does not replace authorization, expiry, replay protection, or state validation.

Design according to the use case

Static first-party content should normally run without JavaScript or native capabilities. Controlled first-party applications may justify a WebView with exact origin rules and a minimal bridge contract.

Third-party browsing and authentication generally belong in platform-supported browser surfaces without access to native bridges.

The objective is not to claim that WebView can become risk-free. It is to prevent one compromised page, dependency, session, or client from automatically inheriting every privilege of the application and backend.

Read the complete article

The full article covers navigation controls, JavaScript, native bridges, local-file access, session handling, TLS, permissions, backend enforcement, cross-platform consistency, runtime monitoring, and a realistic payments scenario.

Read the full article on Medium

How Attackers Abuse Firebase Misconfigurations in Production Apps

Vaibhav Shakya — Thu, 10 Sep 2026 06:02:02 +0000

Firebase configuration embedded inside a mobile application is not the actual security boundary. The real risk begins when production services treat that configuration, an authenticated user, or the application interface as sufficient authorization.

Attackers can reproduce legitimate requests outside the Android or iOS app. If Security Rules allow broad access, hidden buttons, navigation restrictions, and client-side validation cannot protect the underlying data.

Authentication Is Not Authorization

Checking only that request.auth exists proves that someone is signed in. It does not confirm that the user:

Owns the requested record
Belongs to the correct tenant
Has permission to modify sensitive fields
Can perform the requested business transition

Authorization must bind the authenticated identity to the resource and requested operation.

App Check Is Only One Layer

App Check increases confidence that a request came from an expected application environment. It does not establish resource ownership, validate roles, or make client-supplied data trustworthy.

A layered model works better:

Authentication identifies the user. Security Rules authorize data access. App Check increases confidence in the application context. Trusted backends enforce business invariants.

Protect High-Impact Operations

Operations such as settlements, refunds, KYC approval, administrative role assignment, and reward issuance should remain behind a trusted backend.

The backend should validate the actor, current state, permitted transition, input, and idempotency key. It must also handle concurrent requests, retries, replay attempts, and partial failures without repeating a completed business action.

Treat Rules as Production Code

Security Rules, storage permissions, backend identities, and function configuration require:

Version control and reviewed deployments
Cross-user and unauthenticated test cases
Field-level validation
Least-privilege backend permissions
Rate controls and bounded operations
Runtime monitoring and incident procedures

Monitoring and budget alerts improve visibility, but they do not replace authorization or application-level limits.

Final Takeaway

Firebase security does not depend on hiding client configuration. It depends on keeping identity, authorization, application attestation, backend validation, and infrastructure permissions aligned.

A secure design assumes that the client can be inspected, modified, and automated—and protects sensitive operations at the actual data and business boundaries.

Read the complete Medium article:

https://medium.com/@vaibhav.shakya786/how-attackers-abuse-firebase-misconfigurations-in-production-apps-6150c9914076

App Upgrade Architecture: Force Update, Soft Update, Compatibility Windows, and Backend Risk

Vaibhav Shakya — Mon, 07 Sep 2026 05:13:56 +0000

App Upgrade Architecture Is More Than a Dialog

Publishing a new mobile build does not make older clients disappear. Backend releases can happen quickly, while Android and iOS adoption may take days or weeks.

That creates a distributed compatibility problem.

The latest version, recommended version, minimum supported version, and capability-specific requirements should be treated as separate policy decisions. Making every release mandatory can turn a routine deployment into an operational risk.

Evaluate Compatibility by Capability

An older client may still be safe for low-risk operations such as:

Viewing transaction history
Checking settlement status
Reading profile information

However, it may need to be restricted from initiating a payment or adding a beneficiary when those operations require a newer request contract, authentication flow, or risk signal.

This allows teams to restrict the affected capability without unnecessarily blocking the entire application.

The Backend Owns the Operation Boundary

A startup version dialog improves the user experience, but it is not sufficient enforcement. Deep links, background tasks, cached sessions, and modified clients may bypass client-side checks.

Sensitive APIs should independently enforce:

Required request contracts
Authentication and authorization
Input and business-rule validation
Idempotency and retry handling
Valid transaction-state transitions

The declared application version is useful compatibility context, but it is not a security credential.

Hard enforcement should begin only after the replacement build is healthy, compatible, and obtainable by every affected audience. Otherwise, users may be blocked without having a reachable upgrade path.

Read the complete article on Medium

Designing Responsible AI Workflows in Customer-Facing FinTech Apps

Vaibhav Shakya — Fri, 04 Sep 2026 04:59:13 +0000

Responsible AI Needs an Architecture Boundary

Adding AI to a FinTech application becomes more consequential once the model can influence beneficiaries, transactions, KYC workflows, risk decisions, or internal tools.

A useful architectural boundary is:

AI interprets and proposes. Policy authorizes. Domain services enforce.

Even when model output is structured and confident, it should still be treated as untrusted input.

Generated identifiers should be resolved against authoritative systems. Tool access should follow least privilege. Sensitive context should be scoped before inference, and consequential actions should continue through the same authentication, fraud, limit, validation, and business-policy controls used by traditional application flows.

Structured Output Is Not Authorization

A model may return something that looks valid:

{
  "action": "TRANSFER",
  "beneficiaryId": "BEN_3928",
  "amount": 15000
}

But the system still needs to verify whether that beneficiary belongs to the authenticated user, whether the amount is allowed, whether risk checks pass, and whether additional confirmation is required.

Schema validity solves a parsing problem.

It does not solve an authorization problem.

Tool-Using AI Changes the Security Boundary

Once a model can invoke APIs or internal tools, it becomes part of an operational workflow.

The model can propose an operation, but backend services should continue to decide whether it is permitted and whether it can safely execute.

For financial transactions, this also means preserving normal protections around retries, duplicate requests, idempotency, and transaction state.

Responsible AI Starts Before Runtime

Runtime guardrails matter, but many important decisions happen earlier.

Teams should define:

What AI is allowed to influence
What data it can access
Which tools it can use
When customer confirmation is required
When human escalation is appropriate
How model and prompt changes are tested
What happens when AI becomes unavailable
How important decisions can later be reconstructed

The objective is not to make the model perfect.

It is to make sure a model error does not automatically become a financial error.

Read the Full Article

I cover the complete architecture, failure modes, mobile/backend boundaries, tool security, graceful degradation, and an end-to-end payment example here:

https://medium.com/@vaibhav.shakya786/designing-responsible-ai-workflows-in-customer-facing-fintech-apps-267a786d215f