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SolutionsBanking

AI security and governance built for banks.

Keep customer data out of prompts, check consent at each AI request, and hand examiners evidence graded by how it was obtained, with the dates in view.

What regulators ask for

Specs

Banking, in detail

Delivery and data

Delivery
SaaS, from one login.
Isolation
Each customer runs in an isolated workspace with its own database.
Certifications
None held. Frameworks are mapped to and assessed against.

Sources

  1. Reserve Bank of India, FREE-AI committee report (press release), 13 Aug 2025
  2. CERT-In AI security blueprint, as reported by MediaNama, 25 May 2026

Last reviewed 6 Oct 2026

Customer data · at the gatewayIllustrative

Customer data · at the gateway: copilot to ColossalX (prompt); kyc-agent refused before ColossalX (consent withdrawn); payments-bot held for a person before ColossalX (held for approval); ColossalX with data redacted, to provider A (card data redacted).

In short

AI governance for banks, in ColossalX, means knowing which models, agents and third-party AI touch customer data, keeping account and card numbers out of prompts and answers, refusing the next AI request after a customer withdraws consent, and handing examiners evidence graded by how it was obtained.

DSCI and BCG found that AI-specific controls such as formal AI governance, agent monitoring and runtime guardrails are still being built across the sector.

DSCI-BCG, 28 May 2026

The context

Why banks are looking hard at AI now.

Indian financial firms pay the most for a breach, are attacked more often, and are still building AI controls.

INR 40.9 crore

average cost of a breach in Indian financial services INR 40.9 croreaverage cost of a breach in Indian financial services (INR 409 million), the highest of any sector in India. Source: IBM Cost of a Data Breach India 2026, 3 Aug 2026.IBM, 3 Aug 2026

1.6 times

the global average attack rate, for Indian BFSI 1.6 timesthe global average: the rate at which Indian banking, financial services and insurance are attacked. Source: DSCI-BCG, 28 May 2026.DSCI-BCG, 28 May 2026

Over half

of successful attacks on AI agents, via access control and prompt injection Over halfof successful cyberattacks on AI agents through 2029 are expected to exploit access control weaknesses and prompt injection. Source: Gartner, 26 Aug 2026.Gartner, 26 Aug 2026

Banking

Where AI meets customer data, and what holds it.

Four places, each held by a control.

  1. A copilot pastes account and card numbers into a prompt to a model.

    Runtime guardrails

    The controlGuardrails redact personal data both ways, with India banking and payments presets.

  2. A customer withdraws consent, and an agent keeps sending their data.

    Runtime consent

    The controlThe next AI request carrying that data is refused, and the refusal logged.

  3. Model risk teams cannot list the models, agents and third-party AI in use.

    AI inventory and agent map

    The controlOne inventory of models, agents and AI vendors, each with an owner.

  4. An examiner asks for evidence that AI controls ran, and gets a spreadsheet.

    Compliance and AI governance

    The controlEvidence collects itself, graded A to D by how it was obtained.

Regulators and frameworks

What regulators ask for, and how ColossalX relates.

Mapped frameworks, context, and the dates that matter.

Mapped to

  • India DPDPIndia DPDPConsent and purpose limits for personal data, with consent managers.
  • PCI DSSPCI DSSCardholder data protected wherever it travels, prompts and answers included.
  • ISO/IEC 42001ISO/IEC 42001An AI management system: policy, roles, risk and controls.
  • NIST AI RMFNIST AI RMFGovern, map, measure and manage AI risk across its life.
  • EU AI ActEU AI ActFor banks serving the EU: duties scaled to risk, from transparency up.

Context only

  • RBI FREE-AIRBI FREE-AIBoard-approved AI policy, governance and assurance, as recommendations.
  • CERT-In AI blueprintCERT-In AI blueprintAI asset inventories, shadow AI monitoring and emergency shutdown.
Regulatory clockIllustrative

Regulatory clock: 13 Nov 2026 India DPDP consent manager rules start (PIB, DPDP Rules 2025); 13 May 2027 India DPDP core obligations start (PIB, DPDP Rules 2025).

What you see

What you see, and what you can show an examiner.

Lineage from real gateway traffic, beside the evidence pack an examiner can take away and check.

Data lineageIllustrative

An illustrative data lineage map for a bank, drawn from gateway traffic: client KYC records and a card ledger read by named agents, through the gateway to two models. The PAN is withheld at the gateway, the name is sent on, and the cross-border flow is marked.

Evidence pack · an outlineIllustrative

Evidence pack · an outline: Inventory Models, agents and vendors, owned; Runtime Refusals, redactions, approval holds; Consent The log of real refusals; Audit A sealed, timestamped archive. Graded A to D.

What the evidence pack shows4
  • Which agent sent which kinds of personal data to which model, from gateway traffic.
  • Each redaction, refusal and approval hold, with the policy that made it.
  • Evidence graded A to D by how it was obtained, verified by a second person.
  • A sealed, timestamped audit archive, with findings carried into the risk register.

Honest by design

What we will not tell you.

Consent · at the requestIllustrative

Consent · at the request: 10:02:14 Consent withdrawn (signed-in customer, inference context); 10:02:15 Next request refused (kyc-agent, carrying that data); 10:02:15 Refusal logged as evidence.

x, not measured

Runtime consent covers the inference-context purpose for signed-in users, and fails open on error.

All 4 limits
  • RBI and CERT-In publications are context. ColossalX does not map its controls to them.
  • ColossalX holds no certification and is delivered as SaaS only.
  • Redaction finds the identifiers its presets and your own patterns describe, not ones nobody defined.

Questions

Questions buyers ask

What do banking regulators expect for AI governance?

Expectations are converging on board-approved AI policy, an inventory of the models in use including third-party AI, testing and monitoring, and clear accountability. ColossalX maps to India DPDP, PCI DSS, ISO/IEC 42001, NIST AI RMF and the EU AI Act; RBI and CERT-In guidance is explained as context.

How do banks manage AI model risk?

They start with an inventory of the models, agents and third-party AI in use, then test them, monitor them and record who decided what. ColossalX keeps that inventory, attacks your own AI with authorisation and turns findings into owned work and graded evidence.

How does India's DPDP Act affect AI in banking?

India's Digital Personal Data Protection Act, 2023 makes consent and purpose central. When a customer withdraws consent, AI systems using their data have to stop. ColossalX refuses the next AI request carrying that person's data, for the inference-context purpose of signed-in users, with a log of real refusals.

Which frameworks does ColossalX map for banks?

India DPDP, PCI DSS, NIST AI RMF, ISO/IEC 42001, the EU AI Act, SOC 2 and GDPR, alongside SEBI and IRDAI cyber circulars for capital markets and insurance. Frameworks are mapped to or assessed against, never certified; ColossalX holds no certification of its own.

How is customer data kept out of prompts and answers?

Guardrails at the AI gateway detect personal data in prompts and answers and redact it, using presets for India banking and payments, PCI DSS, India DPDP and others, plus your own identifiers. Data lineage then shows which agent sent which kinds of personal data to which model.

Next step

Know your x.

See ColossalX on a bank's questions: which AI touches customer data, and what you can show an examiner.

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