Seismic Zones, Data Center Location and Disaster Recovery Sites – unbundling regulations in the banking sector

| Sajjad Zohir |
3 September 2026 |

ICT governance framework of central banks requires financial institutions to have a robust, sophisticated, manageable ICT infrastructure and application systems. As an essential component, the regulatory body demands these institutions to have Data Center (DC) andDisaster Recovery Site (DRS) in the different seismic zone. My exchanges with the AI in this note began last night in figuring out what these seismic zones may be. I shall outline the exchanges briefly to convey two messages – (i) regulators need to avoid parroting and critically examine terms/concepts that are operationalized to ensure compliance, and (ii) what may be a meaningful way to define seismic zones. I deal with the latter and the first will indirectly get unveiled.

The exchange began with a naïve question, “how many seismic zones are there in Bangladesh?”

AI: Bangladesh is divided into four seismic zones, low, moderate, medium-high and highest risk zones.

Those were from Bangladesh National Building Code (BNBC) meant to capture seismic risk to buildings! It didn’t take me long to figure out that conventional discourse refers to a seismic zone as a specific geographic region that shares a similar frequency and intensity of earthquakes,
classified by its level of geological hazard.

But data centers would have to be built in low-risk areas, not in all ‘seismic zones’ (as defined above)! Thus, I asked AI, “why do you give scales of hazards rather than seismic zones that have high risks?” Interesting responses came, suggesting that “Engineers needed numbers, Risk Involves People, Hazard Involves Nature”, etc.

Finally, I realized the need to be more precise, and we eventually engaged in an exchange on approaches to define ‘seismic zones’ for the purpose of storing large data. Defining “seismic zones requires shifting from standard structural building codes to a Data Continuity and Operational Resilience Framework. Standard codes focus on preventing building collapse to save lives, whereas data center zoning focuses on preventing a nanosecond of downtime or hardware destruction.” (AI)

AI picked up the ‘paradigm shift’ and suggested to create a meaningful seismic zoning map for data storage, using a combination of the following specialized criteria:

1. Peak Ground Acceleration (PGA) and Spectral Acceleration (Sa) – since the likelihood of the ground accelerating horizontally and vertically at specific frequencies is relevant for assessing potential damage due to mechanical head crashes in hard drives, disruptions in solid-state arrays, and fiber-optic alignments (even if the building itself remains perfectly intact).

2. Microzonation (Soil Liquefaction and Amplification) – since impacts vary across soft soil and solid bedrock.

3. Critical Infrastructure Proximity (The “Secondary Hazard” Zone) — distance from fault lines crossing power grids, water lines, and fiber backbones is relevant due to centrality of power (grid electricity or fuel-generated).

4. Non-Structural Component Vulnerability

When AI again produced a green, yellow, orange and red matrix, I insisted on moving away from such hazard color scales. Finally, a proposal was made to implement precise risk reduction for data storage, “must abandon hazard color scales entirely and zone by Failure Domains, Latency Boundaries, and Infrastructure Interdependencies.” Instead of mapping how hard the ground shakes, meaningful data zoning maps how far a failure can travel and where data can safely replicate in real time. Thus, seismic zones are defined based strictly on data engineering and business continuity
metrics. Moving away from “Green to Red” risk scales, a data architect zones by Recovery Objectives: was proposed, as reproduced in Chart 1.

Chart 1: The Functional Architecture Matrix

Data Infrastructure Zone	Primary Hazard Boundary	Maximum Allowable Downtime (RTO)	Maximum Allowable Data Loss (RPO)	Architectural Enforcement
Active-Active Zone	Shared Local Fault Line	0 seconds (Instant Failover)	0 seconds (Zero Data Loss)	Synchronous fiber rings; dual active grid feeds; base-isolated structures.
Disaster Recovery (DR) Zone	Shared Regional Power Grid	< 4 Hours	< 15 Minutes	Asynchronous data replication; 72-hour independent generator fuel storage.
Archival / Vault Zone	Shared Tectonic Plate / Continent	< 24 Hours	< 24 Hours	Cold storage tape/cloud vaults located on stable continental shields.

 

By now, the AI was too keen to develop architecture specific to Target RPO/RTO (how many minutes of data loss or downtime can your organization survive)? I chose not to pursue since I had no such parameters specific to an organization. However, I asked the AI to map these three functional data zones directly onto Bangladesh’s geological structures and fiber-grid infrastructure. By overlaying data resilience requirements onto the country’s physical geography, the proposal was to split Bangladesh into three distinct data storage zones.

Chart 2: Bangladesh Seismic-Data Application Matrix

Geographic Zone	Physical Hubs	Geological Risk Profile	Data Continuity Role	Primary Infrastructure Challenge
Zone 1 (Central)	Dhaka, Gazipur, Narayanganj	High liquefaction; soft soil amplification; local fault lines.	Active-Active Production (Low-latency processing).	High cost of seismic structural engineering; grid congestion.
Zone 2 (Southern)	Khulna, Barisal, Chattogram	Lower seismic risk (West); Submarine cable proximity (East).	Disaster Recovery & International Edge caching.	Vulnerability to coastal cyclones; requires robust flood protection.
Zone 3 (Northwest)	Rangpur, Dinajpur, Bogura	Stable crystalline bedrock; lowest seismic hazard rating.	Deep Archival Vault & Sovereign Cloud backups.	Long-distance fiber routing; lower density of local IT talent.

I avoided reproducing the technical details for general readership. Given the current stock of trained data, moving from traditional geographic hazard maps to functional data infrastructure boundaries was a major ‘paradigm shift’ for the AI! The above is an exercise by a non-technical person and may be considered a preliminary attempt to operationalize the concept of ‘seismic zone’ to protect data flows and storage, so vital for banking operations.

Initial responses to an attempt by the author to get clear views on interpreting ‘different seismic zones’ were frustrating. Decisions on choice of sites among operators as well as regulators may be biased by BNBC (building codes). Such decisions may be better informed if the assistance from AIs are obtained by raising right questions and then critically use those in conjunction with prior knowledge to make decisions.

[A short version was published in The Business Standard, 5th September 2026.
See, https://www.tbsnews.net/thoughts/beyond-seismic-zones-rethinking-banking-data-resilience-1534181]