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Security Topics:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-d876a69eaf","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-security/protocols/\"\u003EData Security Protocols\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-security/solutions/\"\u003EData Security 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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EFINANCIAL DATA SECURITY DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EFinancial data security is the practice of protecting sensitive financial information throughout its storage, processing, access and sharing lifecycle by applying controls that preserve confidentiality, integrity and availability.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-626bcb91ea","text":"\u003Cp\u003EFinancial institutions hold information that cybercriminals can use immediately: credentials that open accounts, payment details that authorize transactions and personal data that supports identity fraud. The potential consequences show up in breach costs: \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://newsroom.ibm.com/2026-07-29-ibm-study-one-in-four-malicious-breaches-are-ai-enabled,-costing-companies-6-million-on-average\"\u003EIBM’s 2026 Cost of a Data Breach Report\u003C/a\u003E puts the average financial services data breach at about $6.3 million, one of the highest industry averages in the study.\u003C/p\u003E\r\n\u003Cp\u003EFinancial data now passes through a wide set of systems that read, analyze and act on it. As organizations connect sensitive records to AI-powered analytics, copilots and agents, security teams must govern how those systems retrieve that data, interpret it and use it in downstream workflows.\u003C/p\u003E\r\n\u003Cp\u003EFinancial data security is a continuing exercise in matching protection to risk. Organizations need to know what sensitive data they hold, where and how it travels, who and what has access to it, whether that access aligns with approved uses, and whether their controls are working as intended.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-financial-data-security-protects":{"id":"title-v2-d0fe400f67","additionalClasses":"anchor-title anchor-title--what-financial-data-security-protects","type":"heading2","lines":["What financial data security protects"],":type":"snowflake-site/components/title-v2"},"text_what-financial-data-security-protects_0":{"id":"text-c8befd9c11","text":"\u003Cp\u003EFinancial data security is the set of policies, processes and technical controls used to protect financial information from unauthorized access, disclosure, alteration, destruction or loss. It protects several classes of sensitive financial information: authentication credentials, bank account details, payment instructions, transaction histories, tax records, credit information, investment activity and personally identifiable information (PII), among other sensitive records.\u003C/p\u003E\n\u003Cp\u003EThe classic security objectives apply:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EConfidentiality:\u003C/strong\u003E Sensitive information remains accessible only to authorized people, applications and services.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EIntegrity:\u003C/strong\u003E Records and transactions remain accurate and protected from unauthorized alteration.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAvailability:\u003C/strong\u003E Authorized users and systems retain access to the information and services they need.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EThe consequences of failure vary with the type of data. Exposure of a transaction history presents a privacy problem, while stolen credentials could lead directly to account takeover. Unauthorized modification of payment instructions, meanwhile, threatens the integrity of the transaction itself.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ESee how Snowflake Trust Center helps teams identify and monitor security risks, review account configurations and address findings within Snowflake’s shared-responsibility model:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-financial-data-security-protects_0":{"id":"embed-4e0a8e02a7","youtubeVideoId":"eUmssb-u6Pw","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_what-are-the-classes-of-financial-data":{"id":"title-v2-5c51d63dc7","additionalClasses":"anchor-title anchor-title--what-are-the-classes-of-financial-data","type":"heading2","lines":["What are the classes of financial data?"],":type":"snowflake-site/components/title-v2"},"text_what-are-the-classes-of-financial-data_0":{"id":"text-ac01e9df00","text":"\u003Cp\u003EFinancial data includes several distinct categories of sensitive information, each with its own usage limitations, exposure risks and protection requirements.\u003C/p\u003E\r\n\u003Ch3\u003EIdentity and authentication data\u003C/h3\u003E\r\n\u003Cp\u003ECredentials and authentication secrets can provide direct access to an account, while Social Security numbers and other identity information can help an attacker impersonate the account holder or get through account-recovery checks. Stolen authentication data is even more useful to an attacker when the same credentials work across multiple systems, or when personal information can be used to gain full control of an account. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.verizon.com/business/resources/T343/reports/2026-dbir-data-breach-investigations-report.pdf\"\u003EVerizon’s 2026 Data Breach Investigations Report (DBIR)\u003C/a\u003E found credential abuse accounted for 15% of initial access in confirmed financial and insurance breaches.\u003C/p\u003E\r\n\u003Ch3\u003EPayment and account data\u003C/h3\u003E\r\n\u003Cp\u003EPayment card numbers, bank account numbers, routing details and payment instructions all support the movement of money. Some data is directly regulated: the Payment Card Industry Data Security Standard (PCI DSS), for example, establishes technical and operational requirements for organizations that store, process or transmit cardholder data, as well as entities that affect the security of the cardholder data environment.\u003C/p\u003E\r\n\u003Cp\u003EBecause exposure and misuse are separate events, protection controls must extend beyond preventing disclosure. Controls should also limit who and what systems are permitted to use payment data, monitor how it’s accessed and flag unexpected changes or transactions.\u003C/p\u003E\r\n\u003Ch3\u003ETransaction and financial-history data\u003C/h3\u003E\r\n\u003Cp\u003ETransaction records, balances, credit histories, investment activity, tax records and insurance claims provide a detailed picture of a customer’s financial life. For an attacker, that information may support fraud or more convincing social engineering. For the institution, unauthorized modification presents a separate concern: a financial record needs to remain both private and trustworthy.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-classification/\"\u003EData classification\u003C/a\u003E gives security teams a practical way to reflect those differences. Once data has been identified and tagged according to sensitivity, organizations can apply the appropriate access, masking, encryption, monitoring, retention and deletion policies more consistently.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-are-the-classes-of-financial-data_0":{"id":"card-v2-7c963a344d","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/fireblocks/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","text":"Read the full case study","linkType":"SNOWFLAKE_INTERNAL"},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EFireblocks provides infrastructure for digital assets and stablecoins, securing $10 trillion in transactions across 550 million crypto wallets. As it unified data across 15 domains in Snowflake, Fireblocks used role-based access control and row-level security to give customers governed access to asset and transaction data.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: Fireblocks"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","alt":"Fireblocks logo","lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--0ed8d846-9c35-47d1-ace5-c3bd1b3aef79/fireblocks.png?quality=85&preferwebp=true","isLcpImage":false,"height":"234","width":"416",":type":"snowflake-site/components/image"},"type":"content-card"},"title_common-threats-to-financial-data":{"id":"title-v2-1098ce1119","additionalClasses":"anchor-title anchor-title--common-threats-to-financial-data","type":"heading2","lines":["Common threats to financial data"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-3a7c36aec7","text":"\u003Cp\u003EFinancial institutions face many of the same attack techniques as other industries, although the concentration of valuable identities, accounts and transaction data makes successful access especially attractive to cybercriminals.\u003C/p\u003E\r\n\u003Cp\u003EIn \u003Ca href=\"https://www.verizon.com/business/resources/T343/reports/2026-dbir-data-breach-investigations-report.pdf\" target=\"_blank\"\u003EVerizon’s 2026 DBIR\u003C/a\u003E, financial and insurance organizations accounted for 3,809 reported incidents and 1,300 confirmed breaches. Exploitation of vulnerabilities represented 22% of initial access, phishing 20% and credential abuse 15%. \u003C/p\u003E\r\n\u003Cp\u003EThose figures point to several common attack paths:\u003C/p\u003E\r\n\u003Ch3\u003ECredential theft and account takeover\u003C/h3\u003E\r\n\u003Cp\u003EPhishing, infostealers, social engineering and credential stuffing all seek the same advantage: access that looks legitimate to the target system. Once an attacker authenticates with a real account, perimeter defenses offer less protection. Stronger authentication, \u003Ca href=\"https://csrc.nist.gov/glossary/term/least_privilege\" target=\"_blank\"\u003Eleast-privilege\u003C/a\u003E access and behavioral monitoring work together, limiting what the account can reach while increasing the chance that unusual activity stands out.\u003C/p\u003E\r\n\u003Ch3\u003ERansomware and data extortion\u003C/h3\u003E\r\n\u003Cp\u003ERansomware increasingly combines operational disruption with data theft. Attackers may encrypt systems, copy sensitive records for extortion or pursue both objectives during the same intrusion. For a financial institution, the resulting incident can affect availability and confidentiality simultaneously. Backups support recovery from encryption or destructive activity, while segmentation, access restrictions and monitoring help constrain how far an attacker reaches before recovery begins.\u003C/p\u003E\r\n\u003Ch3\u003EPrivileged and insider misuse\u003C/h3\u003E\r\n\u003Cp\u003EAdministrators, developers, contractors and service accounts often require broader access than ordinary users. Those privileges also increase the impact of a compromised or misused identity. For this reason, least-privilege design should include privileged identities rather than focusing solely on standard accounts. Organizations need visibility into which privileges exist, how they were granted, when they’re used and whether they’re still necessary.\u003C/p\u003E\r\n\u003Ch3\u003EThird-party compromise\u003C/h3\u003E\r\n\u003Cp\u003EFinancial organizations rarely handle every part of a customer transaction or data workflow themselves. Payment processors, software providers, cloud services, data partners and managed-service providers frequently need some degree of system or data access. This dependency is visible in breach data. Third parties were involved in 34% of confirmed financial and insurance breaches examined in \u003Ca href=\"https://www.verizon.com/business/resources/T343/reports/2026-dbir-data-breach-investigations-report.pdf\" target=\"_blank\"\u003EVerizon’s 2026 DBIR\u003C/a\u003E. \u003C/p\u003E\r\n\u003Cp\u003EA vendor breach can reach the financial institution through credentials, integrations, APIs or data already shared with the provider. Vendor risk management needs a clear picture of those paths rather than relying entirely on the provider’s security certifications.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2":{"id":"title-v2-88b227171e","additionalClasses":"anchor-title anchor-title--four-major-areas-of-financial-data-security-controls","type":"heading2","lines":["Four major areas of financial data security controls"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_197132935":{"id":"text-826e9f5083","text":"\u003Cp\u003EIn practice, financial data protection frequently spans four broad control areas: identity and access, data protection, monitoring and recovery.\u003C/p\u003E\r\n\u003Ch3\u003EIdentity and access controls\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/identity-and-access-management/\"\u003EIdentity and access management\u003C/a\u003E establishes which users, services and applications are authorized to reach financial information and what they’re permitted to do with it.\u003C/p\u003E\r\n\u003Cp\u003EControls commonly include multi-factor authentication (MFA), role-based access control (\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003ERBAC\u003C/a\u003E), least-privilege policies, privileged access management and periodic access reviews. For higher-risk operations, organizations might also require stronger authentication or additional approval before a transaction proceeds.\u003C/p\u003E\r\n\u003Cp\u003EAccess should follow job responsibilities closely enough that a compromised account doesn’t automatically expose every system its owner has ever accessed. As roles change, permissions need to change with them.\u003C/p\u003E\r\n\u003Ch3\u003EData protection controls\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-encryption-end-to-end\"\u003EEncryption\u003C/a\u003E protects readable data by converting it into ciphertext that requires the appropriate key for decryption. Organizations typically encrypt sensitive financial information both at rest and in transit.\u003C/p\u003E\r\n\u003Cp\u003EOther techniques serve different purposes. \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/data-tokenization/\"\u003ETokenization\u003C/a\u003E substitutes a sensitive value with a token while storing the original value separately. It’s widely used to reduce exposure of account or payment information. Dynamic \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/data-masking/\"\u003Edata masking\u003C/a\u003E changes what a user sees, allowing an application or analyst to work with a limited representation of sensitive data while preserving the underlying value for authorized uses.\u003C/p\u003E\r\n\u003Cp\u003EUsed alongside access controls, these techniques reduce unnecessary exposure even when someone has legitimate access to the surrounding data set.\u003C/p\u003E\r\n\u003Ch3\u003EMonitoring and detection\u003C/h3\u003E\r\n\u003Cp\u003EAccess policies describe what should happen. Monitoring provides evidence of what actually happened. Authentication logs, data-access records, network telemetry and application activity help security teams identify unusual behavior: a user querying far more customer data than usual, a service account operating from an unfamiliar location or a privileged identity suddenly accessing systems outside its normal workflow.\u003C/p\u003E\r\n\u003Cp\u003EPatterns typically carry more information than a single event. A legitimate login followed by privilege escalation and large-volume data extraction, for example, deserves a different response from the login alone.\u003C/p\u003E\r\n\u003Ch3\u003ERecovery and resilience\u003C/h3\u003E\r\n\u003Cp\u003EPreventive controls will never eliminate every incident. Backups, tested recovery procedures, incident-response plans and clear escalation paths reduce the operational impact when prevention fails.\u003C/p\u003E\r\n\u003Cp\u003ERecovery also needs validation. A backup that exists but can’t be restored within an acceptable period provides limited resilience, so organizations should test both the data and the process: whether recovery copies are usable, whether dependencies are understood and whether teams know who makes decisions during an incident.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_800305784":{"id":"title-v2-aa7b505340","additionalClasses":"anchor-title anchor-title--financial-data-security-and-regulatory-compliance","type":"heading2","lines":["Financial data security and regulatory compliance"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1682365876":{"id":"text-7d1b5ec661","text":"\u003Cp\u003EFinancial organizations operate under overlapping security, privacy and recordkeeping requirements. Which rules apply depends on the organization, the services it provides, the data it handles and the jurisdictions involved. Compliance programs must translate regulatory obligations into operating controls rather than stopping at the policy level.\u003C/p\u003E\r\n\u003Ch3\u003EGramm-Leach-Bliley Act\u003C/h3\u003E\r\n\u003Cp\u003EThe Gramm-Leach-Bliley Act (\u003Ca href=\"https://www.ftc.gov/news-events/topics/protecting-consumer-privacy-security/financial-privacy\" target=\"_blank\"\u003EGLBA\u003C/a\u003E) governs the treatment of certain consumer financial information by covered financial institutions.\u003C/p\u003E\r\n\u003Cp\u003EThree elements are commonly discussed together:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003EThe Financial Privacy Rule governs how covered financial institutions collect and disclose nonpublic personal information and requires specified privacy notices.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EThe Safeguards Rule requires covered institutions to develop, implement and maintain an information security program with administrative, technical and physical safeguards for customer information.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EPretexting provisions prohibit obtaining customer information through false pretenses.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EIn operational terms, GLBA reaches into data inventory, access controls, risk assessment, service-provider oversight and incident response. The exact obligations depend on which regulator has jurisdiction over the institution.\u003C/p\u003E\r\n\u003Ch3\u003EPCI DSS\u003C/h3\u003E\r\n\u003Cp\u003EPCI DSS provides a baseline of technical and operational requirements for protecting payment account data. The current PCI DSS v4.x framework covers controls across network security, protection of account data, vulnerability management, access control, monitoring and security policy.\u003C/p\u003E\r\n\u003Cp\u003EVersion 4 expanded several areas, including MFA for access into the cardholder data environment and more flexible, risk-based approaches for meeting some security objectives. The Council subsequently published PCI DSS v4.0.1 as a limited revision that clarified existing requirements without adding or removing them.\u003C/p\u003E\r\n\u003Cp\u003EPCI DSS applies specifically to payment-account environments, so it sits within a broader \u003Ca href=\"https://www.snowflake.com/en/data-security/\"\u003Edata security\u003C/a\u003E program.\u003C/p\u003E\r\n\u003Ch3\u003ESEC Regulation S-P\u003C/h3\u003E\r\n\u003Cp\u003EFor covered broker-dealers, investment companies, registered investment advisers, funding portals and transfer agents, amendments to \u003Ca href=\"https://www.sec.gov/rules-regulations/2024/06/s7-05-23\" target=\"_blank\"\u003ESEC Regulation S-P\u003C/a\u003E expanded requirements around incident response and customer information.\u003C/p\u003E\r\n\u003Cp\u003ECovered institutions must maintain written policies and procedures designed to detect, respond to and recover from unauthorized access to or use of customer information. The amendments also include customer-notification requirements for certain incidents and requirements governing service providers.\u003C/p\u003E\r\n\u003Cp\u003EUnder the final rule, affected individuals generally must receive notice as soon as practicable and no later than 30 days after the institution becomes aware of qualifying unauthorized access or use. Covered institutions must also require applicable service providers to notify them as soon as possible and no later than 72 hours after becoming aware of an applicable breach.\u003C/p\u003E\r\n\u003Cp\u003EOther federal, state and international requirements could apply as well. Breach-reporting timelines should be determined from the specific rules governing the organization and incident rather than from a single universal deadline.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1850159722":{"id":"title-v2-49fecde607","additionalClasses":"anchor-title anchor-title--how-to-build-a-financial-data-security-program","type":"heading2","lines":["How to build a financial data security program"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1980997817":{"id":"text-4811b9d00e","text":"\u003Cp\u003EBuilding a financial data security program starts with understanding the data, then connecting that understanding to access, protection, monitoring and recovery. The goal is to make those decisions repeatable as systems, users and regulatory requirements change.\u003C/p\u003E\r\n\u003Ch3\u003EInventory and classify financial data\u003C/h3\u003E\r\n\u003Cp\u003EStart by identifying sensitive data, where it resides and how it moves through various systems. The inventory should include databases, applications, analytics systems, file stores and third-party services, along with the integrations that move data among them. Classification then distinguishes data according to sensitivity and business context.\u003C/p\u003E\r\n\u003Cp\u003EFor each important data set, the organization should be able to answer basic questions: What does it contain? Who owns it? Which systems use it? Who has access? What regulatory requirements apply? Where is it copied or shared?\u003C/p\u003E\r\n\u003Cp\u003EWithout that context, security teams won’t know precisely which information requires which controls.\u003C/p\u003E\r\n\u003Ch3\u003EMinimize access\u003C/h3\u003E\r\n\u003Cp\u003EAccess should correspond to a current business need, with privileges reviewed as people, applications and responsibilities change. For human users, that generally means role-based permissions, MFA and stricter controls for privileged activity. Machine identities need the same discipline: permissions should be limited to the specific systems and actions an application requires, credentials should be managed and rotated, and unused service accounts should be removed rather than left with standing access.\u003C/p\u003E\r\n\u003Cp\u003EPeriodic reviews help identify accumulated access that no longer reflects the role or workflow it was originally created to support.\u003C/p\u003E\r\n\u003Ch3\u003EProtect sensitive values\u003C/h3\u003E\r\n\u003Cp\u003EOnce sensitive fields are identified, organizations can choose controls according to how those values are used. Encryption protects stored or transmitted information. Tokenization can remove original payment or account values from applications that don’t need them. Masking limits what authorized users see in analytical or operational workflows.\u003C/p\u003E\r\n\u003Cp\u003EWhen evaluating tokenization or masking for a particular use case, consider whether the user or system needs to see the original value to complete the work. If the answer is no, reducing exposure lowers the amount of sensitive data available through that workflow.\u003C/p\u003E\r\n\u003Ch3\u003EMonitor access and changes\u003C/h3\u003E\r\n\u003Cp\u003EMonitoring should focus on behaviors associated with misuse as well as ordinary infrastructure events. Useful signals include failed authentication, unusual geographic access, privilege changes, high-volume queries, access to sensitive tables by an unfamiliar role and large exports of customer information. Baselines provide context for those events, helping distinguish ordinary activity from deviations that warrant investigation.\u003C/p\u003E\r\n\u003Cp\u003EThe quality of the underlying audit trail also affects response. Investigators need to connect an identity with the data it accessed, the action it performed and the policies in effect at that point in time.\u003C/p\u003E\r\n\u003Ch3\u003ETest response and recovery\u003C/h3\u003E\r\n\u003Cp\u003EIncident plans deserve the same testing as preventive controls. Tabletop exercises expose unclear ownership and escalation procedures before an actual breach. Recovery exercises verify whether systems and data can be restored within required objectives. In regulated environments, teams should also rehearse the information needed to determine whether notification obligations have been triggered and when the relevant reporting clock started.\u003C/p\u003E\r\n\u003Cp\u003EMetrics should reflect those operating realities. Time to detect, time to contain, percentage of sensitive data covered by appropriate policy, privileged-access review completion, recovery performance and unresolved policy exceptions offer more useful evidence than training-completion rates alone.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_280045103":{"id":"text-b47a920fcd","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cb\u003EQUICK TIP\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EA simple design question helps teams decide whether masking or tokenization could reduce exposure in a particular workflow: Does this user or system require seeing the original value to complete the task?\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_v2_279577432":{"id":"title-v2-c6a285d07e","additionalClasses":"anchor-title anchor-title--managing-third-party-financial-data-risk","type":"heading2","lines":["Managing third-party financial data risk"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1023529046":{"id":"text-9c694d237f","text":"\u003Cp\u003EA vendor relationship changes the institution’s effective access boundary. Before onboarding a provider, security and procurement teams should establish what financial information the vendor will receive, which systems or APIs it will reach, where information will be processed or stored and whether subcontractors participate in the service.\u003C/p\u003E\r\n\u003Cp\u003EA provider’s audit report or certification offers useful evidence, but it typically doesn’t answer every one of those questions. The review should also address authentication, encryption, incident response, data retention, deletion, vulnerability management and privileged access.\u003C/p\u003E\r\n\u003Cp\u003EContracts formalize expectations around security controls, breach notification, audit rights and handling of customer information. Once the relationship is active, those obligations need operational follow-through: reviewing material changes, monitoring access where appropriate and removing credentials or data when the relationship ends.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_2062269890":{"id":"title-v2-39beb4da36","additionalClasses":"anchor-title anchor-title--how-ai-affects-financial-data-security","type":"heading2","lines":["How AI affects financial data security"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1995188915":{"id":"text-2ade7c6e9d","text":"\u003Cp\u003EAI is changing both how attackers operate and how security teams defend financial data.\u003C/p\u003E\r\n\u003Cp\u003EWithin defensive workflows, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E and AI assist with tasks such as correlating security events, detecting abnormal authentication or transaction patterns and prioritizing investigations.\u003C/p\u003E\r\n\u003Cp\u003EAt the same time, AI applications introduce new paths to sensitive information. An assistant that queries customer records, for example, still needs an identity, permissions and rules governing which rows or fields it’s allowed to retrieve. Agents that take actions require equally explicit boundaries around the operations they’re authorized to perform.\u003C/p\u003E\r\n\u003Cp\u003EIn \u003Ca href=\"https://newsroom.ibm.com/2026-07-29-ibm-study-one-in-four-malicious-breaches-are-ai-enabled,-costing-companies-6-million-on-average\" target=\"_blank\"\u003EIBM’s 2026 breach research\u003C/a\u003E, more than one in four malicious breaches involved AI-enabled techniques, while financial services and energy showed the highest concentration of AI-driven attacks among critical-infrastructure sectors. On the other side of the security equation, organizations reporting extensive use of AI and automation in security operations also recorded nearly $2 million lower average breach costs than organizations reporting none.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_655309818":{"id":"text-ae5f442b41","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003ECOMMON PITFALL\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EA common mistake is granting machine identities broad access to sensitive financial data for convenience. Service accounts need tightly scoped permissions, just as human user accounts do.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_v2_549409848":{"id":"title-v2-3d7d657aa2","additionalClasses":"anchor-title anchor-title--financial-data-security-with-snowflake","type":"heading2","lines":["Financial data security with Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1420431564":{"id":"text-6c13185c2e","text":"\u003Cp\u003EFor financial data stored and analyzed in Snowflake, governance policies can sit close to the underlying data rather than being recreated independently in every consuming application.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E provides governance capabilities for data discovery, access control, protection and monitoring. Sensitive-data classification helps identify columns that require additional policy, while masking and row access policies govern what users see when they query protected information.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-column-ddm-intro?\"\u003EDynamic Data Masking\u003C/a\u003E, for example, applies masking policies to columns at query time. Depending on policy conditions and the user’s role, the query result may expose the original value, a partially masked value or a fully masked representation.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/access-history\"\u003EAccess History\u003C/a\u003E provides another piece of the security record. Snowflake records which users and queries accessed supported objects and columns, along with information about objects modified by write operations. When masking or row access policies apply, the relevant policy information is also recorded. Those records support audit and investigation of sensitive-data access.\u003C/p\u003E\r\n\u003Cp\u003EThe same governance principles extend into AI workloads. \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon-context/\"\u003EHorizon Context\u003C/a\u003E (in public preview) applies role-based access control, masking and row-access policies to data queries from people, BI tools and AI agents, helping organizations reuse the permissions already governing enterprise data rather than defining a separate data-access model for each AI interface.\u003C/p\u003E\r\n\u003Cp\u003EFor proactive security posture management, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/trust-center/overview\"\u003ESnowflake Trust Center\u003C/a\u003E continuously monitors account-level security risks. It automatically evaluates Snowflake environments against built-in scanner packages to detect risks like missing MFA, overly permissive network policies and inactive users.\u003C/p\u003E\r\n\u003Cp\u003EFor financial organizations, the result is a tighter connection between data classification, policy enforcement and evidence of access — three capabilities that otherwise tend to fragment as sensitive information moves into new analytical and AI applications.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_4165671":{"id":"title-v2-b51ac8aa21","additionalClasses":"anchor-title anchor-title--keeping-financial-data-secure-across-analytics-and-ai","type":"heading2","lines":["Keeping financial data secure across analytics and AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_319293085":{"id":"text-41772d4687","text":"\u003Cp\u003EFinancial data moves through a growing number of systems, services and automated workflows. The security model has to travel with it. The goal is to keep access, approved use and protection aligned with the data wherever it’s queried, shared or acted on. This approach gives financial organizations a more reliable way to extend security into new analytics and AI environments without rebuilding the security model from scratch each time.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_230298659":{"id":"text-97db5028ee","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EKEY TAKEAWAY\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EFinancial data security works best when controls stay connected to the data as it moves among users, applications, third parties and AI systems. Classification, access governance and monitoring help organizations preserve that connection as the surrounding technology changes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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