SwyftComply AI is a managed application security service from Indusface. It combines AI-driven penetration testing (the same approach attackers use to find business-logic vulnerabilities) with virtual patching at the AppTrana platform edge. Every finding is patched in days, not months. You get full protection not just a longer to-do list.
SwyftComply AI · Beta
Fast finding ≠ fast protection.
SwyftComply AI closes the discovery-to-protection gap.
Custom security policies, created at the edge.
Complete protection with an audit-ready report within 7 days, not months.
SwyftComply AI · Cycle Report
Audit-ready
6
Findings
5
Virtually patched
1
Code fix required
Finding
Severity
OWASP
Status
Account enumeration via reset
High
A07:2021
✓ Blocked
IDOR on transaction records
High
A01:2021
✓ Blocked
Broken function-level auth
Med
A01:2021
✓ Blocked
Session fixation on login flow
Med
A07:2021
Fix req.
✓
Audit-grade · RBI · SEBI · IRDAI · PCI DSS 4.0 · SOC 2 · HIPAA
Indusface · AppTrana
Protecting thousands of applications. Blocking billions of attacks.
Platform metrics
<5 Min
From a DNS change to complete protection
100%
Of apps protected in block mode from day one
<7 Days
From vulnerable to audit-ready
6,500+
Customers protected across 95+ countries
The gap
Finding got 100x faster.
Fixing didn't move.
AI finds vulnerabilities faster every day. Your backlog compounds. Even AI-generated patches need review, regression testing, and a release window.
The only timely fix lives outside the code path — at the edge.
× Without SwyftComply AI
The exposure window stays open for months
Traditional scan-and-report workflow
AI finds the vulnerability in hours. Before your team even knows it exists.
Day 1
Finding joins a backlog your team is already behind on
Week 1
Prioritization, sprint planning, code review
Weeks 2–6
Code fix shipped, tested, deployed to production
Months 2–3
Exposure window
2 to 3 months
✓ With SwyftComply AI
The window closes in days, not months
Discover → Confirm → Protect → Prove
Find what attackers find — every business-logic gap surfaced
Discover
Only real findings reach you — zero false positives
Confirm
Each finding gets its own custom rule at the edge
Protect
Compliance evidence, signed off and ready to share
Prove
Exposure window
Days. Not months.
How it works
Four stages. One outcome.
From first scan to an audit-ready report. Custom policies live at the edge. No code change. No backlog. No long list handed off.
01
DISCOVER
Find what attackers find
AI agents probe your applications and APIs the way an attacker would — reasoning about your specific workflows, not just signature matching. Every business-logic gap surfaced before a hostile actor with AI finds it.
→
02
CONFIRM
Only real findings reach you
Indusface security experts review every finding. False positives eliminated. Severity classified. Each finding mapped to your compliance framework before any policy work begins. Zero noise. Zero chasing.
→
03
PROTECT
Every finding shielded at the edge
Application-specific custom policies authored at the AppTrana platform edge — including the 70% off-the-shelf rulesets cannot cover. Validated against live traffic in monitor mode, then promoted to block. No code change. No deploy window.
→
04
PROVE
Compliance evidence, signed off
Cycle closes with an audit-grade report that satisfies RBI, SEBI, IRDAI, PCI DSS 4.0, SOC 2, HIPAA, and regulator requirements. Each finding mapped to severity, OWASP category, and confirmed protection status. Share with assessors directly.
Real examples
AI finds them.
Experts close them.
See the business-logic vulnerability AI discovers and the custom rule that closes it in days, not months.
Multi-step chain attack · invisible to signature scanners
Loan top-up amount tampering via state desync
The top-up workflow has three steps: customers check eligibility against their repayment history (/api/loan/topup/eligibility), submit the desired top-up amount (/api/loan/topup/apply), and receive disbursement (/api/loan/topup/disburse). Each call authenticates the user and returns 200 OK. To a DAST scanner running endpoint-by-endpoint, every call is legitimate.
What AI agents discover: by checking eligibility for ₹50,000 (which passes against repayment history), then quietly modifying the top-up amount to ₹50 lakh via the application endpoint before the disbursement step fires, the system disburses 100× the verified eligibility. The state desync between the eligibility check and disbursement is invisible to any tool that doesn’t reason about the workflow itself.
01
AI agent maps the top-up workflow by observing how legitimate sessions move through the eligibility, apply, and disburse endpoints
02
Probes for state-desync windows between eligibility check and disbursement — confirms the gap is exploitable
03
Indusface engineer writes a rule enforcing top-up amount immutability between eligibility and disbursement, deployed at the platform edge
04
Rule live in block mode. Finding closed. Audit-ready for RBI NBFC Master Direction and Fair Practice Code
topup_state_desync.log
# AI agent maps multi-step top-up workflow
POST /api/loan/topup/eligibility
customer_id: "C-2287" → 200 · eligible_upto: 50,000
POST /api/loan/topup/apply
amount: 50,000 → 200 · app_id: T-9982
# Chain step: mutate amount AFTER eligibility
PATCH /api/loan/topup/T-9982
amount: 5,000,000 → 200 · updated
POST /api/loan/topup/disburse
app_id: "T-9982" → 200 · DISBURSED ₹50L ✗
₹49.5 lakh fraud · eligibility was ₹50,000
# SwyftComply AI · Day 5 · Rule deployed
RULE swyft_5562 · TOPUP-AMOUNT-INTEGRITY
IF topup.amount changed AFTER eligibility
AND new_amount > eligibility.cap
THEN BLOCK · alert risk team
✓ Deployed: block mode
✓ RBI NBFC Master Direction evidence: logged
✓ Finding closed · Exposure window: closed
POST /api/loan/topup/eligibility
customer_id: "C-2287" → 200 · eligible_upto: 50,000
POST /api/loan/topup/apply
amount: 50,000 → 200 · app_id: T-9982
# Chain step: mutate amount AFTER eligibility
PATCH /api/loan/topup/T-9982
amount: 5,000,000 → 200 · updated
POST /api/loan/topup/disburse
app_id: "T-9982" → 200 · DISBURSED ₹50L ✗
₹49.5 lakh fraud · eligibility was ₹50,000
# SwyftComply AI · Day 5 · Rule deployed
RULE swyft_5562 · TOPUP-AMOUNT-INTEGRITY
IF topup.amount changed AFTER eligibility
AND new_amount > eligibility.cap
THEN BLOCK · alert risk team
✓ Deployed: block mode
✓ RBI NBFC Master Direction evidence: logged
✓ Finding closed · Exposure window: closed
Context-aware finding · parameters legitimate, combination illegitimate
Claim payout inflation via endorsement-state mismatch
The claims engine checks the policy’s sum insured before approving a payout. The endorsement workflow lets policyholders request modifications — increase coverage, add family members — that go to underwriting for approval. Both flows are legitimate. Each individual call passes scanner checks.
What AI agents discover: by submitting an endorsement request to upgrade coverage from ₹5 lakh to ₹50 lakh (which enters “pending” state awaiting underwriter approval), then immediately filing a claim, the claim engine reads the upgraded sum insured from the pending endorsement, while the payout module never checks whether the endorsement was actually approved. A ₹50 lakh payout closes against a ₹5 lakh policy. Each parameter is legitimate; the combination is the flaw.
01
AI agent infers the endorsement state machine and how the claims engine references it across the policy lifecycle
02
Probes 36 endorsement–claim combinations until the state-mismatch payout path is confirmed against test policies
03
Indusface engineer authors a rule requiring endorsement.state = “approved” before claim engine can read upgraded sum_insured
04
Rule live in block mode. Finding closed. Audit-ready for IRDAI cyber-security guidelines and ISO 27001
endorsement_state_mismatch.log
# Policyholder · Sum Insured: ₹5,00,000
POST /api/policy/endorsement/request
policy_id: "P-4471"
upgrade_sum_insured: 5,000,000
→ 200 · status: pending_underwriting
# Immediately file claim (no wait for approval)
POST /api/claims/submit
policy_id: "P-4471"
claim_amount: 4,800,000
→ 200 · claim_id: CL-7821
GET /api/claims/CL-7821/payout-eligibility
→ reads endorsement.upgrade_sum_insured
→ ignores endorsement.state
→ 200 · eligible_payout: 4,800,000 ✗
₹48 lakh payout against a ₹5 lakh policy
# Each parameter legitimate. Combination is the flaw.
# SwyftComply AI · Day 5 · Rule deployed
RULE swyft_3398 · ENDORSEMENT-STATE-GUARD
IF /claims/* reads endorsement.upgrade_*
AND endorsement.state != "approved"
THEN fallback policy.original_sum · alert fraud-IRT
✓ Deployed: block mode
✓ IRDAI / ISO 27001 evidence: logged
✓ Finding closed · Exposure window: closed
POST /api/policy/endorsement/request
policy_id: "P-4471"
upgrade_sum_insured: 5,000,000
→ 200 · status: pending_underwriting
# Immediately file claim (no wait for approval)
POST /api/claims/submit
policy_id: "P-4471"
claim_amount: 4,800,000
→ 200 · claim_id: CL-7821
GET /api/claims/CL-7821/payout-eligibility
→ reads endorsement.upgrade_sum_insured
→ ignores endorsement.state
→ 200 · eligible_payout: 4,800,000 ✗
₹48 lakh payout against a ₹5 lakh policy
# Each parameter legitimate. Combination is the flaw.
# SwyftComply AI · Day 5 · Rule deployed
RULE swyft_3398 · ENDORSEMENT-STATE-GUARD
IF /claims/* reads endorsement.upgrade_*
AND endorsement.state != "approved"
THEN fallback policy.original_sum · alert fraud-IRT
✓ Deployed: block mode
✓ IRDAI / ISO 27001 evidence: logged
✓ Finding closed · Exposure window: closed
Concurrency flaw · the class scanners essentially never find
Inventory oversell via parallel checkout race
Flash sales, festive drops, limited editions. The checkout flow runs an inventory check, reserves the cart, processes payment, then decrements stock. The check-and-decrement happen as two database operations. For about 300 milliseconds between them, the inventory shows as available to every concurrent buyer.
What AI agents discover: during a 100-unit flash drop, firing 300 parallel checkout requests in the race window completes ~247 of them successfully before the inventory counter catches up. The system reports “sold out” to the 248th customer — but 247 orders are already paid for. Cancellations, refunds, brand damage. Single-request DAST testing always sees correct behaviour; only an AI agent that probes concurrency reveals the flaw.
01
AI agent identifies the check-then-decrement timing window during normal traffic observation of the checkout flow
02
Fires 300 parallel checkout requests against a limited SKU, confirms ~247 succeed within the 300 ms DB-write window
03
Indusface engineer authors an edge-level per-SKU mutex for limited-drop inventory; concurrent requests serialised, overflow rejected
04
Rule live in block mode. Finding closed. Audit-ready for PCI DSS 4.0 and SOC 2 (CC7.2 process integrity)
inventory_race.log
# Flash drop · Limited SKU · Stock: 100 units
# t = 0ms
POST /api/checkout sku: SNKR-247 → 200 reserved
POST /api/checkout sku: SNKR-247 → 200 reserved
POST /api/checkout sku: SNKR-247 → 200 reserved
# ... 244 more, all 200 OK within 280ms
# t = 310ms · inventory decrement completes
POST /api/checkout sku: SNKR-247 → 409 sold_out
247 orders confirmed · only 100 units in stock
# Why DAST scanners miss this entirely:
# Single-request testing always sees correct behaviour.
# Race conditions require concurrent probing the scanner
# engine was never built to do.
# SwyftComply AI · Day 6 · Rule deployed
RULE swyft_8847 · INVENTORY-RACE-GUARD
IF /checkout received N>1 in 500ms
WITH same sku · limited_drop flag
THEN serialise · first N=stock accepted · rest 429
✓ Deployed: block mode
✓ SOC 2 CC7.2 / PCI DSS 4.0 evidence: logged
✓ Finding closed · Exposure window: closed
# t = 0ms
POST /api/checkout sku: SNKR-247 → 200 reserved
POST /api/checkout sku: SNKR-247 → 200 reserved
POST /api/checkout sku: SNKR-247 → 200 reserved
# ... 244 more, all 200 OK within 280ms
# t = 310ms · inventory decrement completes
POST /api/checkout sku: SNKR-247 → 409 sold_out
247 orders confirmed · only 100 units in stock
# Why DAST scanners miss this entirely:
# Single-request testing always sees correct behaviour.
# Race conditions require concurrent probing the scanner
# engine was never built to do.
# SwyftComply AI · Day 6 · Rule deployed
RULE swyft_8847 · INVENTORY-RACE-GUARD
IF /checkout received N>1 in 500ms
WITH same sku · limited_drop flag
THEN serialise · first N=stock accepted · rest 429
✓ Deployed: block mode
✓ SOC 2 CC7.2 / PCI DSS 4.0 evidence: logged
✓ Finding closed · Exposure window: closed
Why this is hard
70% of what AI finds, no automated tool can patch. That’s where everyone else stops.
The 30% is generic OWASP territory that any platform can handle. The 70% is your application’s business logic. Off-the-shelf rules can’t see it. Only custom rules, hand-written by humans who understand your workflows, can close it.
30%
Standard rules
Any platform vendor can do this
Generic OWASP Top 10 patterns, signature-based protection. Automated tools handle this category.
70%
Custom policies required
This is where everyone else stops
Business-logic vulnerabilities require security rules written specifically for your application. No automated tool delivers this alone. It needs a managed team that understands your application.
Custom-written policies per finding
False-positive tested against your real traffic
Block mode deployment, not monitor mode
Indusface security engineers own the outcome
Compliance ready
The compliance bar just moved.
From scan to patched.
Auditors aren’t just asking about AI-discovered vulnerabilities anymore. They’re asking about remediation. A clean scan report no longer satisfies an auditor. SwyftComply AI generates an audit-grade, zero-vulnerability report at the end of every cycle, designed to be shared directly with assessors.
RBI
India regulatory alignment
Meets RBI cybersecurity frameworks and supervisory expectations for banks, NBFCs, and regulated financial institutions.
SEBI
Capital market security
Cybersecurity and cyber resilience framework for trading and financial platforms.
IRDAI
Insurance sector posture
Cybersecurity and cyber crisis management guidelines for insurers and intermediaries handling policyholder data.
PCI DSS 4.0
Requirement 6.4 coverage
Application testing and platform-layer remediation for public-facing web applications handling card data.
SOC 2
Application security testing evidence
Testing and remediation documentation generated automatically as part of every cycle.
HIPAA
Technical safeguard documentation
Full scan-to-patch audit trail satisfies application security obligations for healthcare data handlers.
Common questions
Questions we hear from every security team.
SwyftComply AI is in Beta ahead of general availability. The core capability — context-aware AI scanning, expert validation, custom rule authoring, and virtual patch deployment in block mode at the AppTrana platform edge — is production-ready and runs on the same WAAP infrastructure that protects 6,500+ applications today. What Beta means: we’re working with a select group of design partners to refine the cycle, validate the SLA at scale, and tune the audit-grade report format with real GRC and assessor input. Design partners get production deployments, not a sandbox.
Because AI-generated code patches still need code review, regression testing, and deployment windows — and skipping those steps can introduce new vulnerabilities. Stanford’s 2023 study “Do Users Write More Insecure Code with AI Assistants?” (Perry et al.) and subsequent research have shown AI-written code carries higher rates of common security flaws than human-reviewed equivalents. Even with AI in the loop, the code-level patch cycle has an irreducible floor: typically weeks to months from finding to safe deployment. SwyftComply AI sidesteps this entirely. We patch outside the code path, at the platform edge. No code change. No regression risk. No new bugs introduced. Your dev team still ships permanent fixes through normal sprint cycles — but you are protected in days, not months.
That’s exactly why SwyftComply AI exists. The virtual patch deployed at the platform edge protects the application without any code changes. Your dev team can address the underlying vulnerability through normal sprint cycles. The application stays protected while that process runs, for as long as needed. No urgency forced on engineering.
A traditional pentest is a point-in-time engagement that ends in a report — remediation is on your team. SwyftComply AI is also engagement-based, but the engagement doesn’t end at the report. Each cycle delivers both the finding (AI-discovered) and the fix (custom virtual patches at the platform edge, hand-authored by Indusface experts) in days, not months. You run cycles as often as your risk posture and compliance schedule require — quarterly, monthly, or before a major release.
The complete cycle: AI-driven scanning, findings validation, custom rule authoring, false-positive testing, and virtual patch deployment in block mode on AppTrana WAAP. The SLA clock starts when the scan is initiated and ends when all patches are live and verified.
The virtual patch keeps you protected indefinitely. There is no forced urgency on your engineering team. Many SwyftComply AI customers run with virtual patches in place for months while permanent code fixes move through the normal development lifecycle. Discovery never creates a problem you can’t get out of.
Before any virtual patch goes live in block mode, Indusface security engineers test each custom rule against real application traffic. Rules are validated to confirm they block malicious patterns without affecting legitimate requests. This human-in-the-loop step is what makes the guarantee possible. Automated rule deployment without expert validation cannot deliver this confidence.
Yes. The report generated at the end of each cycle is audit-grade and designed to satisfy application security requirements for SOC 2, PCI DSS 4.0, HIPAA, RBI, SEBI, and IRDAI. It includes each finding, severity, OWASP category, and confirmed patch status. Most customers share it directly with assessors without modification.
SwyftComply AI is priced per application, with the full managed service included: AI scanning, expert validation, custom rule authoring, and virtual patch deployment. There are no per-finding fees and no surprise costs when the cycle runs longer. Pricing is aligned to your environment and complexity. Speak with our team for a quote specific to your applications.
No. SwyftComply AI is available to new customers as well. New customers are onboarded onto AppTrana as part of the SwyftComply AI engagement, so the virtual patches can be deployed at the platform edge. The onboarding is managed and takes less than a week.