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The challenge is whether those controls are enforced in the right places, cover the data consistently and actually reduce the amount of operational work security teams must do.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-2cbb6576f5",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"content_chip_copy":{"id":"content-chip-2dc65a8ebb","cta":{"id":"cta","showOutboundIcon":false,"accessibilityLabel":"Laurie 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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-2d143c6598","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/financial-services/\"\u003EFinancial Data Security\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_549182334":{"id":"flexible-column-container-cf85858c84","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-d1a8083f46","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDATA SECURITY SOLUTIONS DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EData security solutions are the capabilities and tools organizations use to discover, govern, protect and monitor sensitive data across the systems and access paths where it’s stored, queried, shared and moved.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-96673a6b5d","text":"\u003Cp\u003EThe data security solutions market is often organized around categories, such as encryption, data loss prevention, access control, posture management, masking and key management. This makes the landscape easier to sort through, but it doesn’t necessarily tell you how well those controls will work across the data estate you actually have.\u003C/p\u003E\n\u003Cp\u003ELooking at \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/\"\u003Edata security\u003C/a\u003E this way also makes tool sprawl easier to see. In a \u003Ca href=\"https://blog.barracuda.com/2025/06/02/new-global-business-research-security-sprawl-increases-risk\" target=\"_blank\"\u003E2025 Vanson Bourne study commissioned by Barracuda\u003C/a\u003E, 65% of security decision-makers said their organizations were juggling too many security tools or vendors, while 53% said their tools couldn’t be integrated with one another. As controls accumulate, organizations have more integrations to maintain, more places where policies have to stay aligned and more opportunities for coverage to diverge.\u003C/p\u003E\n\u003Cp\u003EEvaluating controls by what they protect — and where that protection actually holds — can reveal where tools overlap, where policy depends on repeated integrations, and where coverage starts to break down.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-scope-of-data-security-solutions":{"id":"title-v2-3cb27e2cc5","additionalClasses":"anchor-title anchor-title--the-scope-of-data-security-solutions","type":"heading2","lines":["The scope of data security solutions"],":type":"snowflake-site/components/title-v2"},"text_the-scope-of-data-security-solutions_0":{"id":"text-54f60306fd","text":"\u003Cp\u003EData security solutions include the capabilities and tools organizations use to protect sensitive data at rest, in transit and in use. The market spans discovery and classification; access control; encryption, masking and tokenization; \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/data-loss-prevention/\"\u003Edata loss prevention\u003C/a\u003E (DLP); posture management; backup and recovery; key management; monitoring and audit.\u003C/p\u003E\r\n\u003Cp\u003EUnderneath that category list, most controls are doing one of four jobs: discovering sensitive data, enforcing access, limiting exposure or preserving enough telemetry to investigate what happened.\u003C/p\u003E\r\n\u003Cp\u003EThose jobs are tightly connected in production. Suppose a \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-classification/\"\u003Edata classification\u003C/a\u003E process identifies a column containing customer account numbers. That classification might drive a masking policy for support users, an access policy restricting the column to a finance role, and a DLP rule watching for the data to leave an approved environment. Audit records then show which identity accessed the column and what operation it performed.\u003C/p\u003E\r\n\u003Cp\u003EWhere a control runs determines what it sees — and what it can actually do. Query-time masking sits in the request path, so it can evaluate the caller, object and policy before returning a value. Data security posture management (DSPM) looks across the environment using metadata, permissions and relationships to identify exposure conditions. Endpoint DLP operates at another point in the path, monitoring or restricting sensitive data when it reaches an endpoint or moves through applications and other destinations. The same customer table might be in scope for all three controls, but each sees a different part of the data path and has different enforcement authority.\u003C/p\u003E\r\n\u003Cp\u003EAI workloads bring more machine identities into the architecture. An agent might authenticate, query governed data, invoke a tool under another credential and take a downstream action in one run. Its privileges, credential lifecycle, authorization boundaries and trace data have to fit alongside the controls already applied to human users and service accounts.\u003C/p\u003E\r\n\u003Cp\u003EFor a data team reviewing its security stack, a solution inventory won’t provide a complete picture. To get an accurate view of how well your data is protected, follow a few sensitive data sets through the estate. Which controls touch them? Where are those controls enforced? What controls survive a clone, export or external query path? What changes when the caller is a service account or agent? Those paths tend to expose both gaps and duplication faster.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ESee how Snowflake Trust Center helps teams identify, prioritize and remediate security risks using scanner packages, guided remediation and account-level security checks:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_the-scope-of-data-security-solutions_0":{"id":"embed-945026cb69","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_the-core-categories-of-data-security-solutions":{"id":"title-v2-b84ec2760b","additionalClasses":"anchor-title anchor-title--the-core-categories-of-data-security-solutions","type":"heading2","lines":["The core categories of data security solutions"],":type":"snowflake-site/components/title-v2"},"text_the-core-categories-of-data-security-solutions_0":{"id":"text-14fb53e274","text":"\u003Cp\u003EOnce teams understand the jobs their controls need to perform, the major solution categories help map coverage across the estate. Products often span more than one category, so the useful question is what each capability contributes to the protection path.\u003C/p\u003E\r\n\u003Ch3\u003EData discovery and classification\u003C/h3\u003E\r\n\u003Cp\u003EDiscovery establishes where sensitive data lives; classification attaches context that downstream controls use to decide how that data should be handled.\u003C/p\u003E\r\n\u003Cp\u003EA PII tag, for example, might feed masking, access policy, DLP and DSPM. When several controls depend on the same classification, consistency is vital from an operational standpoint — if three products scan the same column independently, they could produce three different labels and three different downstream policy decisions.\u003C/p\u003E\r\n\u003Cp\u003EReusing an authoritative classification result reduces both duplication and policy drift.\u003C/p\u003E\r\n\u003Ch3\u003EAccess control\u003C/h3\u003E\r\n\u003Cp\u003EAccess controls determine which users, applications, service accounts and other identities may reach specific data and what they’re allowed to do with it.\u003C/p\u003E\r\n\u003Cp\u003EDepending on the system, that might include role-based access control (\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003ERBAC\u003C/a\u003E), \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/row-level-security/\"\u003Erow-level security\u003C/a\u003E, column-level controls or policies derived from attributes attached to the identity, data or session.\u003C/p\u003E\r\n\u003Cp\u003EWhen reviewing access control, look beyond the existence of a policy. Check which identity the system evaluates, where the authorization decision occurs and whether every relevant access path passes through that decision point.\u003C/p\u003E\r\n\u003Cp\u003ELeast privilege also requires maintenance. Roles accumulate permissions, service accounts outlive projects and application access patterns change. Periodic review of actual access against intended access is part of keeping the control effective.\u003C/p\u003E\r\n\u003Ch3\u003EEncryption and key management\u003C/h3\u003E\r\n\u003Cp\u003EFor \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/data-encryption/\"\u003Eencryption\u003C/a\u003E, the architecture around the keys deserves as much attention as the fact that encryption is present. Teams need to know who controls the key material, which principals can use it, how rotation and revocation work and what appears in the audit trail. Those details define the trust boundary around data encrypted at rest or in transit.\u003C/p\u003E\r\n\u003Cp\u003EIf a key is revoked, rotated incorrectly or unavailable, teams need a recovery path that doesn’t undermine the control they need to maintain.\u003C/p\u003E\r\n\u003Ch3\u003EData masking and tokenization\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/data-masking/\"\u003EData masking\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/data-tokenization/\"\u003Etokenization\u003C/a\u003E both reduce exposure of sensitive values, but they solve different problems.\u003C/p\u003E\r\n\u003Cp\u003EDynamic data masking changes what a caller receives at query time. A support user might see only the last four digits of an account number while an authorized finance workflow receives the full value, for example.\u003C/p\u003E\r\n\u003Cp\u003ETokenization replaces a sensitive value with a non-sensitive token. Depending on the implementation, an authorized process may use a token vault, mapping service or other controlled mechanism to recover or reference the original value.\u003C/p\u003E\r\n\u003Cp\u003EChoosing which to use when depends on where the transformation occurs and which workflows still require the raw value. Teams also need to consider what happens when data is copied or consumed by another application.\u003C/p\u003E\r\n\u003Ch3\u003EData loss prevention\u003C/h3\u003E\r\n\u003Cp\u003EData loss prevention identifies and controls sensitive data across endpoints, applications and network or cloud channels, including how that data is used and moves.\u003C/p\u003E\r\n\u003Cp\u003EDepending on where a DLP control is deployed, it might inspect an upload, flag sensitive content in an outgoing message or block a transfer that violates policy. Endpoint DLP sees a different part of the path from a data-platform control, so coverage needs to be mapped accordingly.\u003C/p\u003E\r\n\u003Cp\u003EClassification is typically the connective tissue. If DLP and the data platform use different definitions of sensitive data, the same object may be treated differently depending on where it appears.\u003C/p\u003E\r\n\u003Ch3\u003EData security posture management\u003C/h3\u003E\r\n\u003Cp\u003EDSPM gives teams a view of where sensitive data resides, which identities can reach it and where risky exposure conditions exist across the environments being monitored.\u003C/p\u003E\r\n\u003Cp\u003EA useful DSPM deployment does more than build an inventory. It helps prioritize combinations that deserve attention: sensitive data with broad access, forgotten copies, unmanaged locations or permissions that no longer line up with intended use.\u003C/p\u003E\r\n\u003Cp\u003EA DSPM finding still needs a remediation path. If the tool identifies a table with excessive access, the underlying role, policy or application path has to change, whether that change is automated through an integration or handled by the responsible team.\u003C/p\u003E\r\n\u003Ch3\u003EBackup and recovery\u003C/h3\u003E\r\n\u003Cp\u003EBackup and recovery controls preserve trusted copies so teams can restore data after deletion, corruption, ransomware or another disruptive event.\u003C/p\u003E\r\n\u003Cp\u003EThose copies remain part of the security estate. A backup containing regulated customer data needs appropriate access controls, encryption and retention policies even if nobody queries it during normal operations.\u003C/p\u003E\r\n\u003Ch3\u003EMonitoring and audit\u003C/h3\u003E\r\n\u003Cp\u003EMonitoring and audit controls record enough activity for teams to investigate access, detect suspicious patterns and produce evidence for internal or regulatory review. At a minimum, teams need to answer key questions: Which identity accessed the object? What operation did it perform? When did the event occur? Which policy applied?\u003C/p\u003E\r\n\u003Cp\u003EWhen audit evidence is split across several tools, correlation is also part of the operational workload.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-core-categories-of-data-security-solutions_0":{"id":"card-v2-81b0ff031f","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read the full case study"},"image":{"id":"image","height":"351","src":"/adobe/dynamicmedia/deliver/dm-aid--4c2385f9-311f-4443-a04c-ccbda2232db7/merkle-switzerland-ag%25403x.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Merkle logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["Customer story: Merkle"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EMerkle uses Snowflake to consolidate sensitive customer data and collaborate with clients and partners under shared governance controls. Snowflake Secure Data Sharing and Data Clean Rooms reduce the need for traditional data movement and standalone copies, helping Merkle strengthen privacy controls while making governed data easier to share across organizations.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_where-a-data-security-control-runs-affects-what-it-covers":{"id":"title-v2-bd7dcefed7","additionalClasses":"anchor-title anchor-title--where-a-data-security-control-runs-affects-what-it-covers","type":"heading2","lines":["Where a data security control runs affects what it covers"],":type":"snowflake-site/components/title-v2"},"text_where-a-data-security-control-runs-affects-what-it-covers_0":{"id":"text-6d0e8dccdc","text":"\u003Cp\u003EA control only has authority at the point where it’s enforced. That sounds obvious, but it’s easy to overlook once the same sensitive data is queried or moved through several systems.\u003C/p\u003E\n\u003Cp\u003ETake a masking policy on a sensitive table. When that table is queried through a governed access path, the platform evaluates who’s making the request and applies the masking rule before returning the data. Because the policy is enforced at query time, the control acts directly on the access request rather than detecting the issue after the fact. An external scanner can identify the exposure, but remediation still needs to change the underlying configuration, policy or access path.\u003C/p\u003E\n\u003Cp\u003EPlatform-native controls and specialist tools have different roles. Controls enforced close to the data are well suited to governing access inside the platform and across supported query paths. External tools make sense where the security boundary extends beyond the data platform — endpoints, email, SaaS applications and other environments all need their own controls.\u003C/p\u003E\n\u003Cp\u003EThe issue for data security teams is how much tool complexity represents necessary coverage. \u003Ca href=\"https://www.ibm.com/thought-leadership/institute-business-value/report/unified-cybersecurity-platform\" target=\"_blank\"\u003EResearch from the IBM Institute for Business Value, Palo Alto Networks and Oxford Economics\u003C/a\u003E found that the average organization runs 83 security solutions from 29 vendors, while 52% of executives identified complexity as the biggest impediment to security operations. Using platform-native controls where they meet the requirement can reduce duplicated policy and integration work, while specialist tools continue to cover parts of the estate the platform doesn’t govern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_where-a-data-security-control-runs-affects-what-it-covers_0":{"id":"text-9f44824c50","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDon’t assume two controls that cover the same data provide the same protection. A query-time policy, DSPM scanner and endpoint DLP control see different parts of the data path and have different enforcement authority.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-data-security-solutions-are-delivered-and-deployed":{"id":"title-v2-778d145bc4","additionalClasses":"anchor-title anchor-title--how-data-security-solutions-are-delivered-and-deployed","type":"heading2","lines":["How data security solutions are delivered and deployed"],":type":"snowflake-site/components/title-v2"},"text_how-data-security-solutions-are-delivered-and-deployed_0":{"id":"text-4af4bd94c3","text":"\u003Cp\u003EOnce teams know which controls they need, the next question is how those controls will be operated and where they need to work. That affects staffing, integration effort and the ongoing cost of keeping policy aligned with the data estate.\u003C/p\u003E\n\u003Ch3\u003EData security software\u003C/h3\u003E\n\u003Cp\u003EData security software includes scanners, policy engines, consoles and related products that an organization deploys, configures or operates.\u003C/p\u003E\n\u003Cp\u003EImplementation work varies by product. Depending on the control, teams may need to configure classification rules, policies, alerting, integrations and remediation workflows. After deployment, those configurations still need maintenance as schemas, access patterns and the surrounding environment change.\u003C/p\u003E\n\u003Cp\u003EThat operating cost belongs in the buying decision. A product that generates a high volume of findings with little context for prioritization can increase the security team’s workload even when the detections themselves are accurate.\u003C/p\u003E\n\u003Ch3\u003ECloud data security solutions\u003C/h3\u003E\n\u003Cp\u003ECloud environments distribute data across accounts, regions, object stores, managed services and, frequently, multiple providers. Each boundary can introduce another identity model, policy engine or enforcement point.\u003C/p\u003E\n\u003Cp\u003ECloud data security solutions help teams discover sensitive data across cloud services, assess exposure, enforce or integrate access controls and monitor how data moves between environments. Depending on the product, that can include DSPM, DLP, identity-aware access controls and policy enforcement across cloud-native data stores and services.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-evaluate-data-security-solutions":{"id":"title-v2-95267647b8","additionalClasses":"anchor-title anchor-title--how-to-evaluate-data-security-solutions","type":"heading2","lines":["How to evaluate data security solutions"],":type":"snowflake-site/components/title-v2"},"text_how-to-evaluate-data-security-solutions_0":{"id":"text-0046ceb3a0","text":"\u003Cp\u003EEvaluating a data security solution starts by following representative sensitive data through the architecture your organization uses. Capability checklists help narrow the field, but a proof of concept should show what happens against the identities, copies and access paths the organization actually has.\u003C/p\u003E\r\n\u003Ch3\u003EStart with where sensitive data lives\u003C/h3\u003E\r\n\u003Cp\u003EChoose several representative data sets and trace them. For a customer table, that might include the production object, development copies, replicas, exports, backups and downstream applications. Record which identities access each version and through which interface.\u003C/p\u003E\r\n\u003Cp\u003EThe exercise often exposes coverage gaps quickly. One tool might protect the production table while a copy in object storage sits outside its scope. Another might discover the copy but have no authority to change the access policy around it.\u003C/p\u003E\r\n\u003Ch3\u003EIdentify the enforcement point\u003C/h3\u003E\r\n\u003Cp\u003EFor every control under consideration, ask where the actual decision occurs. Does a policy run when the query is processed? Does a scanner inspect the environment periodically? Does an endpoint agent evaluate the data only after it leaves the platform?\u003C/p\u003E\r\n\u003Cp\u003EThen test alternate paths. If a supported external engine queries the same object, does the policy still apply? If another application reaches the data through a different interface, which identity is evaluated?\u003C/p\u003E\r\n\u003Cp\u003EVendor coverage claims are much easier to interpret once those paths are explicit.\u003C/p\u003E\r\n\u003Ch3\u003ESeparate prevention, detection and reporting\u003C/h3\u003E\r\n\u003Cp\u003EDuring evaluation, identify what happens after a policy condition is met. Some controls block the action immediately, while others simply create an alert.\u003C/p\u003E\r\n\u003Cp\u003ERun a representative finding through the complete workflow. Where does the alert go? Does it contain enough context to assign an owner? Which system makes the remediation? How much analyst work sits between detection and closure? That path is part of total cost of ownership.\u003C/p\u003E\r\n\u003Ch3\u003ECheck whether controls reuse security context\u003C/h3\u003E\r\n\u003Cp\u003EIf several products rely on the same classification, ownership or lineage information, find out whether they consume a shared source or maintain their own copies.\u003C/p\u003E\r\n\u003Cp\u003EIf each tool independently scans and labels a column differently, the organization has multiple classification systems to tune and reconcile. Shared security context reduces that repeated work and gives downstream controls a more consistent basis for policy.\u003C/p\u003E\r\n\u003Ch3\u003ETest what happens to copies\u003C/h3\u003E\r\n\u003Cp\u003ECreate the situations that will eventually appear in production. Clone or replicate a table. Export part of it. Share it with another environment. Query it through another supported interface.\u003C/p\u003E\r\n\u003Cp\u003EThen inspect what remains intact: classification tags, access policies, masking rules, audit coverage and lineage.\u003C/p\u003E\r\n\u003Cp\u003EClaims that a policy “follows the data” should be tested against specific object types and access paths. The mechanism is what tells you whether the protection actually travels.\u003C/p\u003E\r\n\u003Ch3\u003EEvaluate triage cost\u003C/h3\u003E\r\n\u003Cp\u003EThe value of a tool isn’t determined by the number of findings. A product producing fewer findings with stronger context will often demand less operational effort than one optimized for raw coverage.\u003C/p\u003E\r\n\u003Cp\u003ETake a representative sample of findings and work through them. How many are duplicates? How many lack enough context to assign? How often does an analyst have to open another console to understand what happened? How long does it take to identify the owner who can change the policy or data object?\u003C/p\u003E\r\n\u003Ch3\u003EDefine remediation ownership\u003C/h3\u003E\r\n\u003Cp\u003EData security frequently crosses team boundaries. Security may define the policy and accept risk, while the data team operates the system where the control is enforced. An application team may own the access path that created the exposure.\u003C/p\u003E\r\n\u003Cp\u003EAssign remediation ownership before deployment. If a tool identifies broad access to a sensitive table, someone should already know who decides whether that access is legitimate, who changes the relevant role or policy and how an exception is documented.\u003C/p\u003E\r\n\u003Ch3\u003EUse compliance obligations as inputs to the evaluation\u003C/h3\u003E\r\n\u003Cp\u003ERequirements such as PCI DSS, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html\"\u003EHIPAA\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/data-governance/regulatory-compliance/gdpr/\"\u003EGDPR\u003C/a\u003E should feed directly into the control criteria. For each obligation, identify the behavior or evidence the organization needs. Then test whether the solution provides the necessary enforcement, logging, access controls or workflow against the regulated data itself.\u003C/p\u003E\r\n\u003Cp\u003EAnalyst research and peer reviews are useful for scoping the market and building a checklist, but a proof of concept will better answer organization-specific questions: how the product behaves against your identities, policies, data stores and access patterns.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-evaluate-data-security-solutions_0":{"id":"text-02010586d2","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EPick one representative sensitive data set and trace every relevant access path before comparing products. It will surface enforcement gaps, duplicated classification and remediation handoffs much faster than a feature checklist alone.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_securing-data-for-ai-and-agent-workloads":{"id":"title-v2-df1b285db5","additionalClasses":"anchor-title anchor-title--securing-data-for-ai-and-agent-workloads","type":"heading2","lines":["Securing data for AI and agent workloads"],":type":"snowflake-site/components/title-v2"},"text_securing-data-for-ai-and-agent-workloads_0":{"id":"text-dc58af3964","text":"\u003Cp\u003EAI changes who — or what — is requesting enterprise data and how far a single request can travel. An \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003EAI agent\u003C/a\u003E may authenticate, retrieve sensitive data, call another tool under a different credential and use the result to trigger a downstream action — all inside a single run.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003EAI security\u003C/a\u003E introduces more points in the chain to inspect: Which principal is querying the data? What privileges does it have? When the agent invokes a tool, whose credentials are used? Does the downstream call inherit the original authorization boundary, or does it cross into another one? Can the team reconstruct the sequence later from the trace?\u003C/p\u003E\n\u003Cp\u003EAgent privileges should be scoped to the data and actions required for the task. Credentials need rotation and revocation. Audit records need to show the data accessed and tools invoked, while trace data gives teams the intermediate steps needed to understand how one action led to the next.\u003C/p\u003E\n\u003Cp\u003EMany organizations are still catching up on AI access controls. \u003Ca href=\"https://newsroom.ibm.com/2025-07-30-ibm-report-13-of-organizations-reported-breaches-of-ai-models-or-applications,-97-of-which-reported-lacking-proper-ai-access-controls\" target=\"_blank\"\u003EIBM’s 2025 Cost of a Data Breach Report\u003C/a\u003E found that 97% of organizations that experienced an AI-related breach lacked proper AI access controls, while 13% reported a breach involving AI models or applications.\u003C/p\u003E\n\u003Cp\u003EModel-level protections sit alongside those data controls. Prompt injection, indirect prompt injection, model theft, membership inference, data poisoning and other adversarial techniques require testing at the model and application layers as well. Frameworks such as \u003Ca href=\"https://atlas.mitre.org/\" target=\"_blank\"\u003EMITRE ATLAS\u003C/a\u003E give teams a structured way to think through those attack techniques while data security controls continue to govern what enterprise information the system is allowed to reach.\u003C/p\u003E\n\u003Cp\u003EShadow AI raises another problem. When employees or applications send sensitive information to unapproved AI services, the data has crossed outside the organization’s normal \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E path. The IBM research found that 20% of organizations reported a breach involving shadow AI, and organizations with high levels of shadow AI saw average breach costs $670,000 higher than those with low or no shadow AI.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-teams-consolidate-data-security-on-snowflake":{"id":"title-v2-c896e46e15","additionalClasses":"anchor-title anchor-title--why-teams-consolidate-data-security-on-snowflake","type":"heading2","lines":["Why teams consolidate data security on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_why-teams-consolidate-data-security-on-snowflake_0":{"id":"text-98b6dcebf1","text":"\u003Cp\u003ESecurity operations are simpler when classification, policy enforcement and audit work from the same governed data instead of being stitched together across separate products.\u003C/p\u003E\n\u003Cp\u003EWithin Snowflake, classification, tags, masking policies, row access policies and RBAC operate against governed data in the platform. A classification tag attached to sensitive data can feed downstream policy without requiring each control to rediscover the object independently.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E extends governance across data inside and outside Snowflake. For supported Iceberg access paths, Snowflake can enforce masking and row access policies when data is queried from external engines, although the enforcement mechanism and feature availability depend on how the engine accesses the table.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon-context/\"\u003EHorizon Context\u003C/a\u003E adds semantic and business context for people and AI systems working with governed data, while Snowflake Trust Center surfaces posture findings and remediation guidance within the Snowflake environment.\u003C/p\u003E\n\u003Cp\u003EOther controls sit around the trust boundary. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-encryption-tss\"\u003ETri-Secret Secure\u003C/a\u003E combines Snowflake-managed and customer-managed encryption keys, while network and authentication policies control how identities connect to the platform.\u003C/p\u003E\n\u003Cp\u003ECopy reduction also changes the security workload. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-sharing-intro\"\u003ESnowflake Secure Data Sharing\u003C/a\u003E lets Snowflake accounts share live data without copying or transferring it between accounts, while \u003Ca href=\"https://www.snowflake.com/en/product/features/data-clean-rooms/\"\u003EData Clean Rooms\u003C/a\u003E let organizations collaborate under controls that define which analyses can be run against shared data. Fewer standalone copies mean fewer objects to discover, classify, authorize and monitor separately.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_design-for-fewer-policy-boundaries":{"id":"title-v2-9e2d2efd3a","additionalClasses":"anchor-title anchor-title--design-for-fewer-policy-boundaries","type":"heading2","lines":["Design for fewer policy boundaries"],":type":"snowflake-site/components/title-v2"},"text_design-for-fewer-policy-boundaries_0":{"id":"text-7a7c58ac06","text":"\u003Cp\u003EAs data estates grow, the security challenge is less about adding another control than keeping existing controls consistent across the places data is stored, queried and shared.\u003C/p\u003E\n\u003Cp\u003EEffective architectures reduce the number of times teams have to recreate classification, access policy and audit context from scratch. While this doesn’t eliminate the need for specialist tools, it does give them a clearer role. Platform-native controls govern the data paths they can enforce directly, while external products cover the parts of the estate that sit outside that boundary. Over time, that makes the stack easier to reason about, easier to operate and less dependent on stitching the same security logic together in multiple places.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_design-for-fewer-policy-boundaries_0":{"id":"text-fca9184739","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EEvaluate data security controls against the architecture they will actually run in: which data and identities they cover, where enforcement happens and what work has to be repeated when data crosses into another policy boundary.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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