BigID vs Securiti: how they compare in 2026

Machine learning drives the core capability, and the platform around it would still work without it. BigID's discovery and classification page contrasts its own approach against legacy tools in exactly those terms: basic classification relies on static regex, keywords and brittle rules, while BigID uses machine learning, natural language processing, pattern recognition, metadata, context, custom rules and classifier tuning. The pricing page describes metadata collection, search and labeling at petabyte scale, augmented by machine learning. Classification is the foundation the rest sits on, since privacy operations, posture management, retention and deletion all act on what the classifier found. But the product a customer licenses is a platform of bundled applications, and a data discovery product built on pattern matching alone is a coherent product that competitors ship. Generative capability is explicitly optional: the subprocessor list records Amazon Bedrock and Azure OpenAI as providing large language model inference for optional features described in the documentation.

AI appears here in three ways, and none makes the models the product a legal buyer pays for. First, as subject matter: the AI Governance, EU AI Act and NIST AI RMF pages sell the ability to govern a customer's AI, which is managing someone else's models. Second, as infrastructure for the customer: Gencore AI prepares data for training, runs prompt, retrieval and response firewalls, and builds copilots, which is AI plumbing rather than AI doing the buyer's work. Third, as technique inside classification and the DataAI Command Graph. Remove the models and most of the platform remains saleable: data discovery and scanning across a stated thousand or more integrations, a catalog, lineage, a data map producing records of processing activity, data subject request workflow, consent capture, assessment templates and breach notification.

Accuracy is asserted throughout and measured nowhere. The classification pages promise better accuracy, fewer false positives, better precision and classifier tuning, and the comparison with legacy tools rests on being more accurate than pattern matching. No accuracy rate, precision or recall figure, test set or evaluation is published. The vendor's own agreement points the other way: clause 9.6 of the license disclaims any warranty of the accuracy or completeness of data or informational content. The product emits classification labels and risk findings rather than legal assertions, so there is no citation to ground and no authority to open. Its central promise, though, is to classify sensitive data more accurately than the alternative, and nothing published allows that to be tested.

Product copy claims accurate data classification, discovery of shadow and cloud native assets, and unified intelligence. For a discovery product, precision is the measure that matters: a missed store of personal data goes unprotected, and over broad matches create review work. The product copy and the agreement publish no precision or recall figure, benchmark, test set or false positive rate. The platform does not retrieve legal authority or produce citations a reader would open. The agreement warrants substantial conformity with the Documentation, not the accuracy of any classification result.

Autonomy is claimed over consequential actions, and oversight is asserted without a described control structure. The platform is sold on turning findings into action: labeling, minimization, deletion, redaction, quarantine, policy enforcement and remediation workflows, with policies enforced across access, sensitive prompts, data usage and AI responses. Those are irreversible operations on a customer's production data. The oversight that exists is real but partial. The classification FAQ describes validation workflows and classifier tuning, a way to check the model's output before it is trusted, and remediation is described as running through workflows rather than silently. Nothing published states the threshold at which the platform acts without a person, what approval a deletion or quarantine requires, or what happens when a classification is wrong and data is removed because of it.

Oversight is sold as a product, not described as a control over the vendor's own output. Securiti ships real runtime controls: prompt, retrieval and response firewalls for large language models, and Agent Commander for detecting and undoing AI agent mistakes. All of them govern the customer's AI systems rather than Securiti's classification and assessment engine. On its own side, nothing says what runs unattended, what confidence threshold makes the system defer, where a reviewer sits relative to a classification decision, or what happens when a classification is wrong. Assessment Automation implies people write assessments, without describing a check on machine output. The agreement is the only place a supervision expectation appears, and it points at the customer: the product augments rather than replaces professional advisors, and the customer must confer with legal counsel as needed. That assigns responsibility rather than describing a mechanism.

BigID's homepage states that it is trusted by Fortune 500 enterprises without naming one. The certifications and product pages carry association and analyst marks including the Cloud Security Alliance, the EDM Council, the World Economic Forum and a CDMC certified solution logo. The classification page links a Forrester Wave naming BigID a Leader in sensitive data discovery and classification for Q2 2026, and a blog post quotes an IDC MarketScape assessment. Analyst placement is a third party judgment about the market rather than a record of what a customer achieved. No named customer, no dated deployment and no figure for what changed is published. A customer stories page sits under Why BigID.

Named organizations and individuals appear with titles and dates, but with no figures for what changed. The Spotlight Talks series has on the record interviews with a director of global analytics at Dye and Durham, an SVP of product at Walker and Dunlop discussing a 135 billion dollar portfolio, and named executives at Sanofi, Volkswagen and International Flavors and Fragrances, dated April to June 2025. That is interview material, not outcome measurement: no before and after metric, deployment scale or time saving is attached. Analyst recognition is extensive but is not deployment evidence: GigaOm, Frost and Sullivan, IDC MarketScape, Forrester Wave, Gartner Cool Vendor and an RSA Conference Innovation Sandbox win. Section 9.2 of the customer agreement requires customers to join Securiti's reference program and develop a profile with an executive quote and logo, so testimonials are a contract term rather than purely voluntary.

Substantive published commitments on confidentiality and access, without segregation or a statement of what the model providers may retain. Customer Data is the customer's Confidential Information under license clause 6.1 and the customer owns it under 8.2. The receiving party may use it only to perform the agreement and may disclose it only to representatives who need to know under equivalent obligations. The Data Processing Addendum adds that BigID processes personal data only on documented instructions, that authorized personnel are under a duty of confidentiality surviving their employment, and that access is limited to those providing the software. All personal data is deleted within thirty days of termination. Schedule 3 backs that with need to know access control, a privileged access management broker for production and multi factor authentication. Nothing published addresses segregation between customers, or between matters inside a tenant, beyond the general security program. Nothing states what Amazon Bedrock or Azure OpenAI retain when the optional generative features are used.

The architecture is documented in detail, and the data use question is answered in the vendor's favor. In the agreement or its security exhibit: a virtual database instance for each customer separates its data logically and is destroyed when the customer leaves. Personal data the platform identifies is put through a one way irreversible hash, with a commitment that personal data is never captured in clear text in logs or databases. Confidentiality is mutual, with notice and a chance to contest compelled disclosure. The customer owns Customer Data, and all of it is deleted from online systems within one business week of a confirmed request. Against that, section 2.1 grants Securiti a royalty free, worldwide license to use Customer Data both to provide the service and for enhancing product or services, with no carve out for model training and no definition of enhancement. Privilege and work product are not addressed, and there is no position on third party model providers.

A boilerplate disclaimer sits in the terms while the marketing describes compliance outcomes, and nothing addresses where the product's output stops and a legal judgment begins. The platform is sold on proving compliance, generating evidence ready for audit, tracking regulatory and control risk, and supporting regulatory reporting across privacy and AI regimes, which is language about legal status. The only published limit is license clause 9.6, disclaiming any warranty of the accuracy or completeness of data or informational content. That is a warranty disclaimer rather than a statement about advice. Nothing published says that a record of processing, a privacy impact assessment or an AI risk score produced by the platform is not a legal conclusion, that privacy counsel remains responsible for the determination, or which jurisdictions the regulatory content covers. The audience is professional.

A real position on advice versus tooling is published in the agreement itself. The disclaimer at section 5.4 says the customer acknowledges the product is meant only to augment its privacy practices, not to replace legal and other professional advisors, and that the customer is a data controller responsible for the data it collects and its own privacy policies. Section 2.5 adds that the customer takes full responsibility as controller, warrants it has met transparency obligations and obtained consents and legal bases, and must confer as needed with legal counsel to confirm and maintain compliance. That allocates professional judgment to the person relying on the output. No jurisdiction limit is named for the tool's coverage, nothing addresses the supervision or competence duties of the practitioner using it, and no professional guidance is referenced.

Responsible AI principles are published without a mechanism that could be audited, which stands out for a vendor that sells AI governance. The certifications and assessments page has a Responsible AI section stating that BigID applies governance, testing, transparent data practices and privacy by design principles to support fairness, accountability and trust in its AI capabilities. Nothing published stands behind it. There is no governance framework or policy document, nobody named as accountable for the behavior of BigID's own models, no description of what is tested before a classifier ships, no testing result, and no disclosure about uneven classification across data types, languages or populations. The eight certifications on that page are security and cloud assurance standards; ISO 42001, the management standard for AI, is not among them. The platform inventories other organizations' models, scores their risk and produces evidence of their governance. Its own classifiers, whose output decides what is labeled sensitive and what is deleted, carry none of that disclosure.

Nothing published addresses governance of Securiti's own models, which contrasts with what the company sells. Securiti publishes extensive material on AI governance frameworks as product capability, with pages for the EU AI Act, the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications and CDMC, and sells an AI Governance module. None of that is Securiti's own governance. No responsible AI page, AI policy, ethics statement, AI governance committee, named accountable owner, testing regime before release or bias evaluation is published. The site navigation was read in full and has no such page. The security exhibit names a security official and a cross functional Security Council, which is information security governance, not AI governance. A vendor selling AI governance publishes none of its own.

Retention, deletion, access control, subprocessors and incident practice are all published in a security schedule that binds by contract. Schedule 3 of the Data Processing Addendum commits BigID to a documented cryptography policy with encryption of all personal data in transit and at rest. Single sign on with multi factor authentication applies, and every production connection is brokered through privileged access management that logs the user. Access is on a need to know basis with timely removal on role change, alongside training, background checks, anti malware, secure coding to OWASP, and change and patch management. Disaster recovery restores to a separate region, event logs are kept at least one year, and penetration testing is annual. Remediation windows follow CVSS severity: forty eight hours for a zero day, seven days for critical, thirty for high and sixty for medium. Notice of a Security Incident is due within seventy two hours of BigID becoming aware, with investigation and cooperation. Deletion is thirty days from termination, and the customer may export at any time. Subprocessors are listed with purpose and location and carry a thirty day objection right with a termination remedy.

The security exhibit to the customer agreement is specific across most of the ground; a named subprocessor list is missing. Deletion has a stated window: on a request by ticket or email, Securiti deletes all Customer Data from online systems within one business week of confirmation, keeping data only where law requires. Incidents have a deadline and method: notice of a security breach as soon as practicable and no later than seventy two hours after Securiti becomes aware, by email with a read receipt to a designated address. Securiti may not tell third parties without approval, and the customer alone decides whether affected consumers are notified. Access control is role based, reviewed regularly and monitored, with only a stated subset of staff able to reach customer data. Testing has a cadence: annual third party penetration tests and audits, weekly internal scans, and disaster recovery tested twice a year, with a summary available on request. Devices carry at least AES-128 full disk encryption. Third parties appear only as categories such as IT service providers and cloud providers, and the CCPA addendum has the customer approve transfers to unnamed affiliates, providers and vendors.

What the vendor stands behind is published and specific. License clause 10.1 gives an indemnity and defense against third party claims that the software infringes intellectual property rights, with five named exclusions and remedies to procure, modify or replace, or terminate with a pro rata refund. Clause 11.2 caps each party at the annualized fees paid during the current subscription term. Clause 11.3 lifts that cap and the exclusion of indirect damages for breach of confidentiality, the indemnities, gross negligence or willful misconduct, and infringement of the other party's intellectual property. It also sets a dedicated ceiling for BigID's liability for breach of its security, data privacy or data protection obligations, including under the data protection agreement, at the greater of four hundred thousand dollars or twice the annualized fees. For a customer whose whole sensitive data estate is being scanned, that is the exposure that matters. Clause 9.3 warrants substantial conformity with the documentation, with a thirty day claim window and correction or pro rata refund as the exclusive remedy, and 9.6 disclaims accuracy and completeness of informational content. No insurance is stated.

The agreement covers indemnity, a cap, a warranty and insurance in specific terms. The indemnity covers more than intellectual property. Securiti defends the customer against third party claims that use of the product infringes a US patent, copyright, trade secret or trademark, and separately against claims arising from Securiti's use or disclosure of Customer Data in breach of the agreement. Direct damages are capped at the lesser of amounts paid under the order form in the previous twelve months or one million dollars, with consequential damages excluded for both sides and the customer's payment obligations carved out. A warranty commits the product to substantially meet the order form, substantially conform to the documentation and be free of malicious code, with performance again, termination and a pro rata refund as the exclusive remedy. An insurance position is published: errors and omissions, professional liability and cyber cover of at least three million dollars per claim and in aggregate, held through the term and two years after, with thirty days' notice of cancellation. A service level agreement adds 99.5 percent availability, tiered credits and a termination right below 92 percent for three months running. Nothing is specific to wrong AI output.

Breadth of connection is documented in the vendor's own material, without depth an implementer could work from. The platform's premise is reaching data wherever it sits. The classification page lists the classes it covers: structured, unstructured and semi structured data, cloud, SaaS, on premises and hybrid environments, messaging, data lakes, development tools and AI sources including models, agents, copilots, prompts, vector stores and pipelines. A data coverage catalog is published with filters by integration type, and the license prices the subscription partly on the number of data sources and connectors, which confirms connectors are a licensed unit. Depth is not described: nothing says what a given connector reads, in which direction, how often, or what a customer must configure. The systems named are enterprise data platforms rather than practice systems.

Securiti states more than a thousand integrations across data systems and names the principal ones in its navigation: AWS, Google Cloud, Azure, Snowflake and Databricks each carry a dedicated page, with a connectors index behind them. Discovery, classification, access intelligence and lineage all work by reaching into those systems and reading their contents, and for parts of the platform downloadable components are deployed inside the customer's own environment. The integration pages publish no API reference or connector specification, and do not describe the direction or granularity of what moves for any individual system. A separate documentation site at docs.securiti.ai is listed among the company's own internet-facing assets in its published system description.

Deployment options, what changes between them, and processing as distinct from storage are all published. The license agreement defines two deployment types an order must specify: self managed software installed on the customer's own systems, and hosted cloud software. Schedule 3 of the DPA states which security measures apply to which. For an on premises deployment only the certifications and audits, personnel training and confidentiality, and secure coding sections apply, because the rest govern infrastructure BigID does not operate. So what each deployment choice gains and gives up is stated. The subprocessor list gives AWS as cloud infrastructure in the US and EU, with every other subprocessor's location listed. The certifications page adds TX-RAMP Level 2 for Texas public sector workloads, and the list names a separate partner used only for FedRAMP deployments. DPA 4.1 discloses that BigID may transfer personal data internationally to its personnel and subprocessors under Standard Contractual Clauses, the UK Addendum and the EU-US Data Privacy Framework. Schedule 3 records that BigID is remote first. Regions are given only as US and EU.

The deployment model is stated plainly, with partial residency detail. The published system description sets out a multi tenant cloud service on AWS and GCP, with a virtual database instance for each customer providing logical separation, plus downloadable components that run inside the customer's own environment. Residency is real: instances sit in several geographically distributed data centers, and each instance serves customers from a specific geography as a standalone offering with no data exchange between instances, which answers the processing question directly. Two clouds are identified by their endpoints: a Global Production Cloud and an EU Production Cloud at app.eu.securiti.ai with its own status page. Resilience detail is published: daily backups copied to a data center in another region, a pilot light disaster recovery strategy, failover across availability zones, and a stated 24 hour RTO and RPO. The full list of regions is not given beyond the EU and Global clouds, and nothing describes what changes between deployment options.

Certification is real, named and committed in the contract, without reachable evidence or dates. The certifications and assessments page lists ISO 27001, SOC 2, a SOC 3 report for general use, CSA STAR with a completed CAIQ self assessment in the STAR Registry, CDMC, PCI DSS and TX-RAMP Level 2, alongside a Wiz recognition. Schedule 3 of the DPA turns two of those into obligations. BigID will maintain a certificate from a reputable third party certification authority evidencing compliance with ISO/IEC 27001. It will obtain and maintain an SSAE18 SOC 2 Type II report covering any system or process used in processing personal data, which is a scope statement rather than a badge. Reports are available on written request no more than once a year, and the DPA separately allows a third party risk assessment satisfied by a questionnaire such as a SIG Lite. No auditor, audit period, certificate number or report date is published. The public page says BigID is aligned with ISO 27001, while the binding schedule commits it to hold a certificate.

The certifications are real and named, and none of the evidence can be reached without asking. Securiti states SOC 2 Type II certification, with the report available on request to prospective and current customers, and holds ISO 27001:2022 and ISO 27701:2019, the privacy information management standard. The agreement backs the SOC 2 position, committing Securiti to provide its latest SOC 2 report or an industry standard successor on request. The published system description names AWS and GCP and the shared responsibility split, annual third party penetration tests and audits, weekly internal scans, and a disaster recovery test summary on request. There is no trust center and nothing to download. No auditor or certification body is named for any of the three, and no certificate date, examination period or scope appears. Every route to the evidence is a request.

The providers are named on a published list with their role and location, and the models are not. The subprocessor page, last modified 7 July 2026, records two entries for large language model inference: Amazon Web Services through Bedrock and Microsoft through the Azure OpenAI Service. Each is described as supporting optional features and located in the US, alongside the wider list of infrastructure, storage, search, authentication, monitoring and analytics providers. That shows whose models process customer data when the generative features are switched on, and that they are optional. No model or version is named, and no commitment is given about where inference runs beyond the US location on the list. Change notification is soft on the page itself, which says BigID will endeavor to give notice of new subprocessors to the extent the agreement requires. The DPA supplies a firmer mechanism: a maintained list, notification and a thirty day objection right with a termination remedy.

At the core of the platform is a knowledge graph, and the product set covers classification across structured and unstructured data, preparing data for vector databases, curating and cleaning data for model training and tuning, and prompt, retrieval and response firewalls for large language models. The product pages name no model, model family, provider or architecture. Nothing says whether a third party foundation model is called in classification or in the copilot products. AWS and GCP are named as infrastructure, which says where workloads run, not whose models they are. No commitment to notify customers when the models change is published.

Every quote is scoped to the data sources, apps and connectors covered, now including AI assistants, models and agents, to the deployment type, and to the level of services and support, and a quote requires contact. Three packages are named on BigID's pricing page, Express, Business and Enterprise, each described by the size of the program it fits and each with its own request for pricing. A free risk assessment is offered. The license agreement adds the mechanics. Fees are set in an order and payable in US dollars within thirty days, with one percent monthly interest on late amounts. Under a license true up, the customer monitors its own usage against the licensed data source and volume metrics, must notify BigID if it exceeds them, must certify compliance on request up to twice a year, and pays excess fees for overage. That term is a real cost exposure. No figure, band or rate appears anywhere.

No figure or tier is published, but the charging unit can be read from the agreement. Modules are procured by use case, and the pricing page ends in a request for a personalized quote. The customer agreement caps the number of Authorized Users at the figure in the order form, which makes named users the unit of charge. Fees are quoted and paid in US dollars. Payment obligations cannot be canceled, are not prorated for partial months and are not refundable. Late payment carries interest of one and a half percent a month, and subscriptions renew automatically for terms equal to the first unless either party gives thirty days' written notice. The service level agreement adds credits of four, six and ten percent against availability levels. A buyer can see the shape of the commercial relationship in advance but learns nothing about cost: no rate, band, package or tier name is published.

Buyer functions are addressed by name. Security teams are served through posture management, cloud data loss prevention, access governance and insider risk; privacy teams through data subject requests, consent, records of processing and impact assessments; and AI governance teams directly. Environment coverage is stated across cloud, SaaS, on premises, hybrid and AI systems, and regulatory coverage has its own published page. Reach into regulated sectors is evidenced through TX-RAMP Level 2 for Texas state systems, a FedRAMP deployment path and PCI DSS assessment. No jurisdiction, regulation, data type or organization size is named as out of scope, the Fortune 500 claim carries no basis, and nothing says what the platform does not cover. Nothing describes what a law firm, as opposed to an in house function, would do with the platform.

Coverage is documented across industries, regulations and technology, and the boundary is left open. Six industries have dedicated pages: financial services, healthcare, telecom, retail, travel and hospitality, and manufacturing. Regulations are named, with pages for GDPR, California's CPRA, Brazil's LGPD, Canada's PIPEDA, China's PIPL, the EU AI Act, the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications and CDMC, behind a wider list. Technology coverage is put at more than a thousand integrations, with the major cloud and data platforms named. Nothing says where the product stops: no organization size, data volume, or environments and obligations it does not reach. The Roles navigation offers Data+AI Builders, Data Security, Data Privacy, Data Governance and Marketing, with no page for legal or counsel, although the privacy pillar is work a data protection officer or privacy counsel owns and the agreement tells the customer to confer with legal counsel.