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.
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.
From first scan to an audit-ready report. Custom policies live at the edge. No code change. No backlog. No long list handed off.
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.
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.
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.
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.
See the business-logic vulnerability AI discovers and the custom rule that closes it in days, not months.
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.
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.
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.
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.
Generic OWASP Top 10 patterns, signature-based protection. Automated tools handle this category.
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.
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.
Meets RBI cybersecurity frameworks and supervisory expectations for banks, NBFCs, and regulated financial institutions.
Cybersecurity and cyber resilience framework for trading and financial platforms.
Cybersecurity and cyber crisis management guidelines for insurers and intermediaries handling policyholder data.
Application testing and platform-layer remediation for public-facing web applications handling card data.
Testing and remediation documentation generated automatically as part of every cycle.
Full scan-to-patch audit trail satisfies application security obligations for healthcare data handlers.
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 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.