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Press Kit — Zytra Technologies

Company overview, key statistics, product descriptions, partner information, and media contact. Use any content on this page freely — attribution to Zytra Technologies appreciated.

Company Boilerplate

Short (50 words)

Zytra Technologies builds AI governance and safety for banking and financial services. Zytra's platform — Aval (runtime guardrails), AI Governance Module, and FinProof benchmark — is purpose-built for BFSI, mapping to RBI, SEBI, DPDP Act, EU AI Act, and NIST AI RMF. Deployed in the bank's own tenant.

Standard (100 words)

Zytra Technologies is the AI governance and safety platform purpose-built for banking and financial services. Unlike enterprise governance tools retrofitted for AI, Zytra starts from the regulator's question: "Can you prove this AI system is safe, fair, and compliant?" Every product answers with evidence, not policy documents.

Zytra's platform covers the three lines of BFSI AI defence: Aval (runtime guardrails at 0.5% FPR and 11.6ms inference latency), the AI Governance Module (living risk atlas, residual risk scoring, signed evidence ledger), and FinProof (the open BFSI AI safety benchmark). Clause-mapped to RBI AI/ML Circular (2025), SEBI Algo-AI Guidelines, DPDP Act 2023, EU AI Act, NIST AI RMF, and ISO 42001. Deploys in the bank's own VPC or on-premises.

Long (200 words — for analyst reports and case studies)

Zytra Technologies is an AI governance and safety company focused exclusively on banking and financial services. Founded in Hyderabad, India, Zytra addresses the governance gap between the pace of AI deployment in BFSI and the sophistication of the risk management infrastructure around it.

Zytra's platform operates across three dimensions. At the inference layer, Aval provides runtime guardrails: 22 BFSI threat categories (prompt injection, PII exfiltration, financial advice violations, regulatory breach, and 18 others), 0.5% AgentHarm false-positive rate, and 11.6ms inference latency. At the governance layer, the AI Governance Module maintains a living AI risk atlas — 141 risk factors, six domains, clause-mapped to all major regulatory frameworks — with residual risk scoring, GRC workflow automation, and hash-chained evidence trails for RBI examination. For evaluation, the open FinProof benchmark provides BFSI-specific adversarial testing across 13 attack categories, with results published on a public leaderboard.

Zytra's founding platform partner is Pennant Technologies (pennApps Lending Factory), through which Zytra governs 14M+ loans and 100M+ annual transactions across India's leading HFCs and NBFCs. Zytra is deployed exclusively in the customer's own cloud tenant or on-premises — no cross-tenant data.

Key Statistics

0.5%
AgentHarm false-positive rate (Aval v1.5, #1 FinProof)
11.6ms
Aval inference latency (inline, production BFSI)
22
BFSI threat categories covered per inference
141
Risk factors in the AI risk atlas
13
FinProof attack categories (B-01 through B-13)
6
Regulatory frameworks mapped (RBI, SEBI, DPDP, EU AI Act, NIST, ISO 42001)
14M+
Loans under governance (Pennant partnership)
60+
Native integrations (ServiceNow, Purview, LangChain, AWS Bedrock, and more)

Products

Aval — Runtime Guardrails

Inline BFSI AI guardrail. 22 threat categories, 0.5% FPR, 11.6ms. Governs both direct user inputs and indirect injection via RAG retrieval. BFSI-specific classifier trained on financial services adversarial data — not adapted from a generic content filter.

AI Governance Module

Living AI risk atlas with 141 risk factors across six domains, clause-mapped to RBI, SEBI, DPDP, EU AI Act, NIST AI RMF, and ISO 42001. Residual risk scoring (Inherent × (1 − Control Effectiveness)), GRC workflow automation, hash-chained evidence ledger, board reporting.

FinProof Benchmark

Open BFSI AI safety benchmark. 13 attack categories (B-01–B-13) drawn from production BFSI incidents. Public leaderboard with reproducible test methodology. Aval v1.5 holds #1 position: 0.5% AgentHarm FPR, 0.994 HackaPrompt recall. Free to run against any BFSI AI system.

Prism — Agentic Governance

Governs multi-turn and multi-agent AI workflows. Authorization boundaries, tool-call audit trails, and forensic replay for agentic deployments (D-09 prompt injection, D-10 authorization bypass, D-11 multi-agent threats). Purpose-built for collections agents and complex document processing pipelines.

Red-Teaming

Adversarial evaluation of BFSI AI deployments against the FinProof benchmark. B-01 through B-13 attack categories, multilingual adversarial inputs, FPR measurement as a first-class metric. External red-teaming produces more reliable evidence for regulatory review than internal testing.

KV Cache Governance

The only platform governing shared KV caches in LLM inference. Tenant isolation enforcement, cache poisoning detection, and forensic replay — reconstructing exact model context state at any point in time for regulatory investigation. Board-submittable evidence, not logs.

Partners & Ecosystem

Founding Platform Partner

Pennant Technologies

Zytra's AI governance stack is integrated into pennApps Lending Factory — the platform powering four of India's top-10 HFCs and top-10 NBFCs. 14M+ loans, 100M+ annual transactions under governance. Full case study →

Event Partner

ISACA Chennai Chapter

Zytra ran Workshop 2 (ASSURE) at ISACA Chennai Conference 2026 — an AI Guardrails Lab engaging CISOs, model risk officers, and auditors on BFSI AI security and RBI compliance. Workshop recap →

Academic collaborators: SRM Institute of Science & Technology (Tamil Nadu), NRI Institute of Technology (Andhra Pradesh). Innovation ecosystem: Amaravati Quantum Valley.

Regulatory Coverage

Zytra is the only AI governance platform with native, clause-level mapping to all of the following:

India (primary)
  • RBI AI/ML Circular (January 2025)
  • RBI Draft eMRM Framework (June 2026)
  • SEBI Algo-AI Guidelines
  • DPDP Act 2023
  • PSS Act — Payment System obligations
Global
  • EU AI Act (High-risk financial services)
  • NIST AI RMF 1.0
  • ISO/IEC 42001:2023
  • SR 11-7 / SR 26-2 (US Federal Reserve)
  • OWASP LLM Top 10 / MITRE ATLAS

Media Assets

Logo
Zytra wordmark (SVG)
Dark background version. Available on request.
Logo
Zytra emblem (SVG)
Icon-only mark. Available on request.
OG Image
Social share image (1200×630)
Download og-image.png →
Screenshot
Platform dashboard
Available on request via media contact.
Benchmark data
FinProof leaderboard
View leaderboard →
Case study
Pennant Technologies
Read case study →

Additional assets — high-res images, infographics, executive headshots — available on request. Contact media team →

Community Outreach Templates

Ready-to-use copy for sharing Zytra's work with the AI governance community. Edit as needed.

OWASP LLM Top 10 Community

GitHub Issue / PR Comment

Target: github.com/OWASP/www-project-top-10-for-large-language-model-applications — Issues & Discussions
We've been tracking how OWASP LLM Top 10 maps to production BFSI deployments. A few findings that might be useful for the financial services section: LLM01 (Prompt Injection): Indirect injection via RAG retrieval is the most common attack vector in banking AI we see — customer-uploaded documents that embed instructions into the retrieval context, not the direct query. We published a full mapping at: https://zytratechnologies.com/post-owasp-llm-top10-bfsi-india-2026.html LLM06 (Sensitive Information Disclosure): In BFSI, PII classification needs to distinguish between customer PII (regulated under DPDP Act, GDPR) and internal model information (system prompts, regulatory reporting logic). Standard PII classifiers don't make this distinction. We also maintain the FinProof benchmark — open BFSI AI safety eval covering B-01 through B-13, with FPR as a first-class metric. Happy to contribute data to any OWASP initiative that would benefit from BFSI-specific test cases. Repository: https://github.com/Tejasvichand/v0-zytra
NIST AI RMF Community

NIST AI RMF Playbook / Tools Submission

Target: airc.nist.gov (AI Resource Center) — Tools & Resources submission form
Tool Name: Zytra AI Governance Platform Organization: Zytra Technologies URL: https://zytratechnologies.com AI RMF Function Alignment: - GOVERN: AI risk atlas (141 risk factors, 6 domains), board reporting, policy enforcement - MAP: Capability-pattern tagging, deployment-level risk assessment, framework crosswalks - MEASURE: Residual risk scoring (Inherent × (1 − Control Effectiveness)), FinProof benchmark - MANAGE: GRC workflow automation, issue tracking, signed evidence ledger, audit trail export Sector: Financial services (BFSI) — banking, HFCs, NBFCs Deployment: On-premises / VPC (no cross-tenant data) Geography: India (primary), Global Notes: Platform includes the open FinProof BFSI AI safety benchmark (https://zytratechnologies.com/research.html), which provides sector-specific adversarial test cases for NIST MEASURE functions. Mapped to NIST AI RMF 1.0 at clause level. Happy to provide additional documentation on request.
LinkedIn — AI Governance Community

Post template (with link back)

Target: LinkedIn posts, AI Governance and BFSI Risk groups
Most AI governance tools tell you what controls to implement. Very few tell you whether those controls are actually working. The difference matters in BFSI because RBI's eMRM framework (June 2026) doesn't ask for a policy document — it asks for evidence. A hash-chained audit log of what your guardrail did, when, and on which inference. Not a diagram of how it was designed. We mapped the specific evidence artifacts RBI requires to the control types that produce them: https://zytratechnologies.com/solutions-india.html For risk and audit professionals: the ASSURE workshop recap from ISACA Chennai 2026 has the four-question checklist we use to evaluate any BFSI AI deployment from a governance standpoint: https://zytratechnologies.com/post-isaca-chennai-ai-guardrails-workshop-2026.html #AIGovernance #BFSI #RBI #ModelRisk #AICompliance
GitHub Awesome Lists

PR to awesome-ai-governance / awesome-llm-security repos

Target: github.com/AthenaCore/awesome-responsible-ai, awesome-llm-security repos
## Tools ### BFSI / Financial Services - [Zytra Technologies](https://zytratechnologies.com) — AI governance platform purpose-built for banking and financial services. Runtime guardrails (Aval: 22 BFSI threat categories, 0.5% FPR, 11.6ms), AI risk atlas, and regulator-ready evidence trails. Mapped to RBI, SEBI, DPDP Act, EU AI Act, NIST AI RMF, ISO 42001. Includes [FinProof](https://zytratechnologies.com/research.html) — open BFSI AI safety benchmark. ### Benchmarks - [FinProof](https://zytratechnologies.com/research.html) — Open BFSI AI safety benchmark. 13 attack categories (B-01–B-13) drawn from production banking incidents. Public leaderboard. FPR (false-positive rate) as a first-class metric alongside recall.
BankingTech / IBS Intelligence / Finextra

Press outreach email

Target: editorial@bankingtech.com, editorial@ibsintelligence.com, editorial@finextra.com
Subject: BFSI AI Governance: India's RBI eMRM Framework and what banks need to do by [enforcement date] Hi [Editor name], I'm writing from Zytra Technologies — we build AI governance infrastructure for banking and financial services, and we've been closely tracking India's evolving AI regulatory stack (RBI AI/ML Circular, the June 2026 eMRM framework, SEBI's Algo-AI guidelines, DPDP Act). One angle that might interest your readers: most BFSI institutions have AI governance policies but not AI governance evidence. RBI's eMRM framework draws a sharp line between the two — and the banks that are furthest along are building evidence infrastructure now, before inspections start. We've published a mapping of what that evidence looks like in practice: https://zytratechnologies.com/solutions-india.html We also maintain FinProof — the only open BFSI AI safety benchmark (https://zytratechnologies.com/research.html), which a number of banks are using as an independent evaluation framework before deploying new AI systems. Happy to provide a briefing, data points, or a contributed piece for your readers. Our founding platform partner is Pennant Technologies (pennApps Lending Factory, 4 of India's top-10 HFCs). Best, [Name] Zytra Technologies https://zytratechnologies.com
NASSCOM / IBA / CAFRAL

Research contribution / speaker proposal

Target: NASSCOM Knowledge Portal, IBA Bulletin, CAFRAL working paper
Subject: Proposed contribution — AI Risk Management in Indian Banking: Evidence vs. Policy We'd like to propose a short research contribution or speaker session on AI risk governance in Indian BFSI, with a focus on the operational gap between RBI's eMRM requirements and current bank practice. Key points we can address: 1. The evidence-vs-documentation distinction: what RBI's eMRM framework actually requires (hash-chained audit logs, not policy documents) 2. The false-positive rate constraint: why BFSI guardrails that flag >2% of legitimate transactions create more regulatory risk than they mitigate 3. The agentic AI governance gap: how SR 11-7 / RBI MRM frameworks are insufficient for autonomous AI agents operating in lending workflows We publish open research at https://zytratechnologies.com/blog.html and maintain the FinProof benchmark — the only BFSI-specific AI safety evaluation framework. Our founding platform partner is Pennant Technologies (India's leading BFSI software platform). We'd be happy to contribute a piece, present at an event, or provide data for a market study. Contact: https://zytratechnologies.com/contact.html

Media Contact

Zytra Technologies
Hyderabad, India

For media enquiries, analyst briefings, speaker requests, and partnership announcements: contact form →

All content on this page may be used freely in editorial coverage. Attribution to Zytra Technologies with a link to zytratechnologies.com is appreciated but not required.

Quick Links

FinProof Benchmark → Pennant Case Study → ISACA Workshop Recap → India RBI/SEBI Compliance → AI Governance Platform → Aval Runtime Guardrails → AI Risk Management Guide → All Research →