Latest news with #dataGovernance
Yahoo
5 days ago
- Business
- Yahoo
BigID Introduces First Data-Driven Assessment for AI Governance and Third-Party AI Use
New capability gives organizations visibility into third-party AI use, data exposure, and governance gaps across their vendor ecosystem. NEW YORK, June 16, 2025 /PRNewswire/ -- BigID, the leader in data security, privacy, compliance, and AI data management, today announced the launch of Vendor AI Assessment, the first solution of its kind designed to help organizations identify, evaluate, and manage the risks introduced by third-party AI usage. As vendors race to embed GenAI, large language models (LLMs), and autonomous agents into their products, organizations are left in the dark about how AI is being used - and what risks it introduces to their data, privacy, and compliance. Today, BigID becomes the first company in security or privacy to launch a dedicated, data-driven assessment solution focused specifically on vendor AI use. Expanding on its leading capabilities in vendor management and third-party risk, BigID now enables organizations to assess not just who they do business with, but how those vendors are using AI and what impact that AI has on sensitive data. Unlike traditional governance tools that rely on static surveys, BigID discovers deployed models, maps them to the data they access, and provides actionable risk intelligence across AI usage, exposure, explainability, and regulatory readiness. For the first time, security, privacy, and legal teams can hold vendors accountable for AI transparency, ensuring they understand whether vendor AI is trained on customer data, whether results can be trusted, and whether the risks are worth the rewards. According to BigID's 2025 AI Risk & Readiness Report, 64% of organizations lack visibility into AI risk exposure, and nearly half have no AI-specific security controls in place. These findings reveal a growing blind spot in enterprise governance: third-party AI use. While many organizations are still building internal AI oversight, BigID helps extend that visibility to a critical but often overlooked threat vector - vendor AI. Key Takeaways: Identify and reduce third-party AI risk before it impacts your business with the industry's first solution to assess vendor AI use. Uncover vendor AI usage, data access, and training practices to mitigate unwanted data exposure and improve governance. Operationalize AI oversight with built-in workflows for risk scoring, documentation, and remediation. Equip privacy, legal, security, and compliance teams to respond to AI-related regulatory demands, especially as 55% of organizations report being unprepared for emerging AI regulations. Stay ahead of AI-driven third-party threats with continuous visibility, faster risk-based decisions, and defensible governance across your ecosystem. "AI adoption is accelerating, but most organizations remain blind to how their vendors use AI on their data," said Dimitri Sirota, CEO of BigID. "We built Vendor AI Assessment to help security, privacy, and legal teams uncover these blind spots, reduce exposure, and ensure responsible use of AI across their third-party ecosystem." "BigID continues to innovate with Vendor AI Assessment. Given the rapid integration of AI in vendor offerings, businesses must demand transparency and accountability," said Dr. Edward Amoroso, CEO of TAG & Research Professor at NYU. "BigID's Vendor AI Assessment provides a crucial tool for organizations to understand and mitigate the unique risks posed by third-party AI use." Learn More: Book a demo Read more at About BigID BigID empowers organizations to know their enterprise data and take action for data-centric security, privacy, compliance, AI innovation, and governance. Customers deploy BigID to proactively discover, manage, protect, and get more value from their regulated, sensitive, and personal data across their data landscape. BigID has earned numerous accolades, including being highlighted as CRN's top 100 security companies two years in a row in 2024 and 2023, a finalist in CRN's 2024 Tech Innovator Awards, recognized as the most innovative security company of the year for its AI data security in the 2024 Globee Awards, and named as a "Market Leader Data Security Posture Management (DSPM)" in the 2023 Global InfoSec Awards. Additionally, BigID's impressive growth earned it a spot on the 2024 Deloitte 500 for the fourth consecutive year, one of CNBC's Top 25 Startups for the Enterprise, named to the Forbes Cloud 100, and recognized on the 2024 Inc. 5000 for the fourth consecutive year. View original content to download multimedia: SOURCE BigID Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
23-06-2025
- Automotive
- Yahoo
Startline Motor Finance names regulatory reporting manager
UK's Startline Motor Finance has appointed Chris Garman as the regulatory reporting and oversight manager. In his role, Garman will ensure accurate regulatory reporting and enhance oversight across Startline. His responsibilities also include promoting data-driven insights to track operational risks and ensuring favourable outcomes for customers. Garman, a University of Stirling graduate, previously served as director of the group data and analytics office at HSBC. He previously worked as vice president of technology and information risk at Morgan Stanley and as an analyst for the national intelligence policing model at the Association of Chief Police Officers in Scotland. Startline Motor Finance CEO Paul Burgess said: 'We are very pleased to welcome Chris to the team. With an ever-higher regulatory focus being an established part of motor finance, he will play a key role in shaping internal operations in a manner that puts the customer first, ensuring compliance and building long-term trust.' Garman commented: 'Given the increasing focus on data-led oversight from the Financial Conduct Authority and the complex landscape of consumer credit regulation, I will be helping ensure that Startline maintains compliance with all relevant requirements. 'My aim is to drive transparency, and continually improve data quality and governance, as well as being ready to readily adapt to future regulatory change to minimise disruption.' A recent survey by Startline showed that more than seven out of ten UK motorists are open to purchasing vehicles from emerging Chinese car manufacturers. In the survey, 72% of respondents expressed willingness to consider Chinese cars. BYD emerged as the most recognised brand among potential buyers, with 28% awareness, followed by Maxus at 19% and Chery at 14%. Other brands such as Aiways, Denza, and Jaecoo each garnered 11%, while Omoda and Xpeng stood at 10%. Nio, Skywell, and GWM Ora achieved 9%, with Leapmotor, Lynk & Co, HiPhi, and Zeekr trailing. "Startline Motor Finance names regulatory reporting manager" was originally created and published by Motor Finance Online, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
Forbes
23-06-2025
- Business
- Forbes
Enterprise Data Management (EDM): Everything You Need To Know
Editorial Note: We earn a commission from partner links on Forbes Advisor. Commissions do not affect our editors' opinions or evaluations. Enterprise data management (EDM) refers to the systems and strategies an organization uses to govern its data so that it's accurate, accessible and secure throughout its lifecycle. It's far more than just infrastructure. EDM turns data into a usable business asset that informs decisions, streamlines operations and shapes customer experiences. As companies handle larger and more complex data environments, EDM plays an even greater role in how information is organized, shared and applied across teams. What Is Enterprise Data Management (EDM)? Enterprise data management (EDM) is the framework organizations use to manage data across systems, teams and workflows. It supports both day-to-day operations and long-term strategic goals by making the policies, procedures and technologies accurate, accessible, consistent and secure. These qualities not only support operational efficiency but also uphold data quality, integrity and governance throughout the information lifecycle. Rather than acting as a passive data repository, EDM structures and maintains information in ways that actively support business strategy. It brings structure and oversight to fragmented data environments, turning scattered inputs into a cohesive system where data is properly categorized, quality-checked and made accessible to the right people at the right time. A national retailer might connect point-of-sale data with warehouse inventory systems to avoid overstocking products that aren't selling. This kind of coordination turns raw information into something immediately useful. Effective EDM strategies rely on tools like data warehouses, where information is aggregated and analyzed, or data lakes, where raw data is stored for future use. The goal is to turn data into a valuable asset that provides insights and drives business growth. In fact, recent industry studies by 451 Research shows that companies using data lakes and similar architectures often see stronger results than those that don't. Over half of surveyed enterprises already have a data lake in place, and nearly a quarter plan to implement one within the next few years. These organizations are more likely to use technologies like artificial intelligence and real-time data streams to guide decisions, improve efficiency and spot revenue opportunities ahead of their competitors. Benefits of Implementing EDM When data lives in too many places or follows different rules, it becomes harder to trust, harder to use and easier to overlook. EDM helps fix that by creating some much-needed consistency—so teams can spend less time cleaning up data and more time putting it to work. When data comes from different systems or teams, inconsistencies are almost guaranteed. EDM helps clean that up by applying the same standards to how data is collected and stored. With fewer discrepancies, it's easier to work with information you can actually trust. Decisions fall apart when the data behind them is unreliable. EDM helps organize information so it reflects what's actually happening, giving teams the context they need to make choices with more confidence. An IBM survey found that 42% of enterprises with over 1,000 employees have already deployed artificial intelligence across their operations. When paired with EDM, these technologies help organizations interpret patterns faster and respond to problems with fewer delays. Say a restaurant group notices customer complaints about a popular seasonal dish. With EDM, they can compare that feedback to actual sales data and identify whether the problem lies with the recipe, pricing or timing, rather than guessing or relying on anecdotal feedback. Without a clear system, even simple tasks can get hung up. EDM makes sure information is stored in the right place and kept up to date, which helps teams move faster and communicate better. For example, a small accounting firm could sync timesheets with its billing platform to automatically generate invoices. Without EDM, that process might involve manual data entry, email chains and unnecessary delays. Laws around data use aren't optional, and the stakes are high. EDM helps keep things in check by setting clear standards for how information is handled, which makes it easier to stay in bounds and catch issues early. EDM reduces waste by avoiding duplication and cleaning up storage. That kind of clarity can also point to smarter ways to serve customers, plan products or improve how the business runs overall. Challenges in Enterprise Data Management EDM offers clear advantages, but building and maintaining a system that actually works takes real effort. Many businesses run into roadblocks that limit the value of their data or prevent teams from using it effectively. Understanding these challenges is the first step toward solving them. One of the most common issues is that data lives in too many places. Different departments use different tools, collect different metrics and store information in ways that don't easily connect. Over time, this creates blind spots, duplication and a lot of wasted effort trying to piece things together manually. Breaking down these silos often requires more than just new technology. Yes, cloud-based storage or integrated platforms can help. But just as important are clear policies, shared standards and leadership buy-in. Without that kind of coordination, even the best tools won't fix the problem. As companies gather more data, they also face more risk if something goes wrong. Whether it's customer records, financial details or internal reports, every system that stores or moves data can become a target. Data breaches not only hurt reputations, but they can also bring legal trouble and serious financial penalties. A good EDM approach includes security from the ground up. That might mean stronger encryption, better access controls or more frequent audits. But no system is secure if people don't know how to use it safely. Training, awareness and clear protocols all play a major role in reducing exposure. In a healthcare setting, EDM might control who can view patient information based on job role. Billing staff would only see what's relevant to insurance processing, while clinicians retain access to the full medical record. Getting different systems to talk to each other isn't always straightforward. Older tools may not support modern data formats. New platforms might require custom connectors. And in fast-moving businesses, those integrations often need to be updated or replaced to keep up with changing needs. A logistics company using outdated inventory software might still want to upgrade its customer relationship platform. EDM allows those tools to connect through middleware, so shipment data can pass between them cleanly. While middleware and APIs are common solutions, they're only effective when integration is seen as an ongoing process, not a one-time project. That means revisiting system design, checking for redundancies and planning ahead for future upgrades, not just reacting when something breaks. What works for a 50-person company can break at 500. As the volume and variety of data grows, systems that once felt organized can become cluttered and unreliable. Reports run slower, access controls become harder to manage and storage costs climb without warning. This doesn't mean you can just buy more capacity. Instead, you need to think ahead by choosing tools and processes that can evolve with your business. Cloud infrastructure helps, but only if it's configured with flexibility in mind. Regular audits of data workflows can flag bottlenecks before they can slow things down. Enterprise data management isn't something most teams can handle without specialized experience. It often requires people who know how to work with databases, understand privacy laws, manage cloud systems and connect tools that don't naturally talk to each other. That level of technical fluency can be tough to find, especially for smaller businesses. In fact, a recent Gartner survey reported that 45% of data and analytics leaders consider skill and staffing shortages a top challenge in implementing their data strategies. Even companies with strong infrastructure can fall short if they don't have enough qualified people to support and evolve their systems. Instead of hiring a full in-house team from the start, many companies take a phased approach. Some bring in consultants or contractors to set up systems and train internal staff. Others focus on building skills within the team they already have. Either way, documenting processes and creating clear guidelines early on makes it easier to grow into a more self-sufficient setup over time. Key Elements of Enterprise Data Management Most businesses manage many different types of data, such as customer records, transactions, inventory, vendor details, employee files and financial information. Managing that data isn't just about storage. EDM focuses on organizing, securing and using it in ways that make the information useful across the organization. Here's what you need to know. Useful data starts with accuracy. That means checking for errors, filling in missing details and keeping formats consistent. EDM systems can help with automation, but people still need to decide what 'clean' looks like. Teams should set clear standards, document them and train regularly so bad data doesn't quietly pile up in the background. Data governance in EDM is about crafting unique, organization-specific policies. A policy on paper isn't enough. The rules have to be built into everyday processes. This involves assigning clear roles and responsibilities for data management and creating a culture where data is treated as a valuable asset. It might include forming cross-functional teams that meet regularly to discuss data-related challenges and successes, ensuring governance is not just policy-based but also practice-oriented. Modern data security in EDM goes beyond traditional measures and incorporates advanced encryption methods, cutting-edge access control technologies such as biometric verification and even the use of blockchain for heightened data integrity. Conducting regular, innovative security training exercises ensures the team is always prepared for new types of cyber threats. Most organizations use multiple systems, which means data is scattered by default. Data integration focuses on bringing together diverse data sets in a meaningful way. Custom API integrations, cloud-based data blending tools and state-of-the-art data management platforms can merge data from various sources. This not only combines data but also enriches its interpretability, making it more useful for different business divisions, as it gives teams the full picture rather than just a stack of disconnected data points. Enterprise data management gives businesses a way to manage information with purpose. Instead of dealing with disconnected tools or outdated records, companies can build systems that make their data easier to trust and use. That helps teams work more efficiently, avoid mistakes and meet regulatory requirements without scrambling. When done right, EDM turns information into something practical—a tool for making smarter decisions and solving real problems. Frequently Asked Questions (FAQs) EDM (enterprise data management) is an overarching approach that focuses on how data is stored, shared and used across an entire organization. In contrast, MDM (master data management) is a subset of EDM that specifically targets the management and governance of critical business data such as customer or product details. A company might use one platform to collect information from sales, billing and customer service . This lets teams see what's happening across departments without jumping between systems or chasing missing details. Enterprise data management typically includes a wide variety of data types such as customer analytics , transaction records, employee details, financial data, supply chain information and operational metrics. This data is gathered from various sources within the organization to support decision-making and business processes. Examples of enterprise data management software include SAP Master Data Governance, Oracle Data Relationship Management, IBM InfoSphere and Microsoft SQL Server Master Data Services. These tools help organizations manage, consolidate and use their data efficiently.
Tahawul Tech
19-06-2025
- Business
- Tahawul Tech
'It's time to stop managing storage and start managing data.' Charles Giancarlo, Pure Storage CEO
Pure Storage has introduced the Enterprise Data Cloud (EDC), a bold new standard in data and storage management simplicity that enables organizations to focus on business outcomes, not infrastructure. Fuelled by AI, data volumes are rising and business demands are evolving faster than ever. Traditional storage models create fragmentation, silos, and uncontrolled data sprawl. Organizations must adapt by shifting their mindset from managing storage to understanding how, where, and why their data is used. This will empower companies to reduce risks, costs, and operational inefficiencies. Solving the Problem of Data Management with an Enterprise Data Cloud An EDC is an industry-changing architectural approach to data storage and management. It gives organizations the ability to easily manage their data across their estate with unrivaled agility, efficiency and simplicity. With an EDC architecture, IT teams centrally manage a virtualized cloud of data with unified control — spanning on-premises, public cloud, and hybrid — enabling intelligent, autonomous data management and governance across the entire environment. Delivering the Enterprise Data Cloud with the Pure Storage Platform With an EDC architecture, organizations are better equipped to manage data at scale, reducing risk, and gaining increased control and insight across all environments. Redefining how data is delivered, governed, and consumed, the Pure Storage platform allows customers to build out their own EDC. The platform gives organizations the ability to unify data from across their estate into a virtualized cloud of data that is governed by an intelligent control plane for easy management, and delivered as a service. 'It's time to stop managing storage and start managing data. With AI increasing the potential value of enterprise data, and cyber-threats imperiling it, data storage architectures and the tools for managing data have not kept pace. Only Pure Storage has innovated an architectural approach that enables enterprise customers to manage their global data estate. Pure Fusion allows customers to create their own global Enterprise Data Cloud empowering them to manage their data with the control, automation and tracking needed to lead in a data-driven world,' said Charles Giancarlo, Chairman and CEO, Pure Storage. At the heart of this autonomous platform is Pure Fusion™, unifying storage as a pool of adaptable resources. Pure Fusion is natively built into the arrays, which are self-discoverable so they can automatically discover a broader fleet without requiring in-depth storage admin configuration. Administrators can manage the fleet from any system because every array is an endpoint. NEW: Pure Fusion with Workload Automation Pure Fusion now has presets and remote provisioning for fleetwide file, block and object. Admins have increased flexibility based on the specific needs of each workload and no longer need to pre-plan and tune deployments, which reduces risk of non-compliance and improves resiliency by ensuring that workloads are provisioned correctly from the beginning. Reducing the Risk of Human Error and Strengthening Security Today, enterprises struggle with increased risk, lack of compliance and inefficiencies resulting from manual operations around provisioning, migration and more. To eliminate these issues, automation spans the full stack of the platform with policy-driven orchestration and self-service capabilities. Built-in compliance and improved cyber resilience embedded across the platform further minimize risk through security and governance policies. These new capabilities completely redefine intelligent storage management. NEW: Workflow Orchestration The Pure Storage platform now delivers orchestrated workflows that can be deployed across the entire IT environment. Built on the thousands of existing connectors to third-party applications including Cisco, Microsoft, VMware, ServiceNow and Slack, presets and application 'recipes' can be easily deployed across storage, compute, network, database and application configurations. Customers will be able to run pre-set recipes or build custom ones specific for their environment, or utilize partner recipes for application to infrastructure automation. NEW: World Class Anomaly Threat Detection with Rubrik Security Cloud Rubrik is the first cyber recovery partner to integrate with Pure Fusion and its new workflow orchestration, streamlining cyber recovery across data environments. When Rubrik Security Cloud detects a threat, Pure Fusion automates the tagging of indelible SafeMode snapshots with Rubrik's ransomware scanning — pinpointing clean data for fast restore. For surgical or granular recovery needs, Rubrik backups provide a secondary path. Managed through Pure1 Workflow Automation, this integration reduces manual effort, improves compliance, and delivers near-zero RTO — so organizations can recover quickly and confidently with minimal disruption. NEW: CrowdStrike LogScale and Pure Storage Hunt Threats and Retain Logs CrowdStrike and Pure Storage have partnered to deliver the first validated on-premises storage solution specifically optimized for Falcon LogScale deployments. Combining Pure Storage's resilient, secure, high-performance storage infrastructure with Falcon LogScale's powerful log analytics, instant search, and security capabilities, organizations gain unmatched scalability and accelerated threat detection, hunting, investigation and response — while maintaining the control of on-premises, self-hosted environments. NEW: Pure Protect VMware to VMware Recovery Now offering recovery for VMware to VMware, in addition to recovery to AWS, on-premises to cloud, and self-service disaster recovery assessments, Pure Protect™ is designed for today's hybrid environments, streamlining recovery workflows with on-demand recovery and flexible failover options so customers can cost-effectively maintain business continuity. NEW: AI Copilot is now Generally Available The AI Copilot is an always-on assistant that delivers personalized, fleet-aware insights — with agents available for topics including security information, performance issues, digital commerce, sustainable operations, and support center. Matt Kimball, Vice President & Principal Analyst, Moor Insights & Strategy, noted, 'Pure's Enterprise Data Cloud represents a tangible shift in how enterprises manage data and represents real change at the architectural level. By abstracting the complexity of hybrid environments into a unified, policy-driven platform, Pure is enabling organizations to bring clarity and control to data management at scale. With automation, intelligence, and simplicity built in, Pure is delivering on the vision of an enterprise data cloud in a way that's actionable today. It's a bold, thoughtful approach — and one that sets a new bar for the industry.' The full benefits of an Enterprise Data Cloud require a platform created from the ground up to enable this model. Pure Storage brings together infrastructure, intelligence, and integrated services into one consistent experience.
Al Bawaba
19-06-2025
- Business
- Al Bawaba
IT Leaders optimistic on Agentic AI, but Concerned by Organizational Readiness, Research Reveals
As AI adoption accelerates and cyber threats increase, nearly 8 in 10 IT security leaders recognize their security practices need transformation. Salesforce's latest State of IT data also reveals unanimous optimism about AI agents, with 100% of security leaders identifying at least one security concern that could be improved by despite this hope, the global survey of over 2,000 enterprise IT security leaders highlights significant implementation challenges ahead. Nearly half (48%) worry their data foundation isn't set up to get the most out of agentic AI, and over half (55%) aren't fully confident they have the appropriate guardrails to deploy AI it matters: Both the professionals charged with protecting a company's data and systems and the bad actors looking to exploit vulnerabilities are increasingly adding AI to their toolkits. Autonomous AI agents, which help security teams cut down on manual work, can free up humans' time for more complex problem solving. However, agentic AI deployments require robust data infrastructure and governance to be perspective: 'Trusted AI agents are built on trusted data. IT security teams that prioritize data governance will be able to augment their security capabilities with agents while protecting data and staying compliant,' said Alice Steinglass, EVP & GM, Salesforce Platform, Integration and Alkhotani, SVP and GM, Salesforce Middle East, said: 'The latest State of IT report is a cause for both optimism and concern, and also aligns with the concerns we see among organizations in the Middle East. While the research underscores the confidence that organizations have in agentic AI to improve key aspects of their operations and processes, it also reveals significant concerns that must be addressed: It is clear that many IT security leaders are concerned about issues including the readiness of their organization's data foundation for AI, the state of their guardrails to deploy AI agents, and the potential for compliance challenges stemming from AI. Amid these anxieties, it is vital that organizations in the Middle East work with a trusted partner such as Salesforce, enabling them scale up agentic AI quickly, effectively, and ethically.'Security budgets ramp up as threats evolveIn addition to a familiar slate of risks like cloud security threats, malware, and phishing attacks, IT leaders now cite data poisoning — in which malicious actors compromise AI training data sets — among their top concerns. Resources are rising in response: 75% of organizations expect to increase security budgets over the coming regulatory environments add a wrinkle to AI implementationWhile four-fifths of IT security leaders believe AI agents offer compliance opportunities, such as improving adherence to global privacy laws, nearly as many (79%) say they also present compliance challenges. This may stem in part from an increasingly complex and evolving regulatory environment across geographies and industries, and is hampered by compliance processes that remain largely unautomated and prone to error.• Just 47% are fully confident they can deploy AI agents in compliance with regulations and standards.• 83% of organizations say they haven't fully automated their compliance is a cornerstone of successful AI, yet confidence is nascentA recent consumer study found that trust in companies is on a precipitous decline, and three-fifths (60%) agree that advances in AI make a business's trustworthiness more critical. Furthermore, only 42% of consumers trust companies to use AI ethically, a decrease from 58% in 2023. IT security leaders see work to be done in earning this critical trust.• 57% aren't fully confident in the accuracy or explainability of their AI outputs.• 60% don't provide full transparency into how customer data is used in AI.• 59% haven't perfected their ethical guidelines for AI governance is a linchpin in enterprises' agentic evolutionNearly half of IT security leaders aren't sure they have the quality data to underpin agents, or that they could deploy the technology with the right permissions, policies, and guardrails, but progress is being made. A recent survey of CIOs found that four times as much budget was allocated to data infrastructure and management than AI, a signal that organizations were smartly laying the right groundwork for broader agents offer a salve as adoption ramps upAccording to the State of IT research, over 40% of IT security teams already use agents in their day-to-day operations — a figure that's anticipated to nearly double over the next two years. IT security leaders expect a range of benefits as their use of agents ramps up, ranging from threat detection to sophisticated auditing of AI model performance. Three quarters (75%) expect to use AI agents within two years — up from 41% overhauls are on tapIn addition to the steps these teams must take to shore up their data foundations for the agentic era, over half admit they have work to do to bring their overall security and compliance practices up to par. Forty-seven percent believe their security and compliance practices are fully prepared for AI agent development and customer view: Arizona State University (ASU) is among the first universities to leverage Agentforce, Salesforce's digital labor platform for augmenting teams with trusted autonomous AI agents in the flow of work. ASU stresses the need for data relevancy, especially as the university advances its AI initiatives. ASU implemented Salesforce-acquired Own backup, recovery, and archiving solutions, providing ASU with a comprehensive approach to data management, addressing their needs for backup, recovery, compliance, and innovation deeper:• Read the full State of IT: Security report• Learn how Salesforce is powering a smarter agentic future with new governance enhancements• Discover additional State of IT insights from the developer perspective• Read more on why trust and guardrails are even more critical in the age of AIMethodology: Data is sourced from a security, privacy, and compliance leader segment of a double-anonymous survey of IT decision-makers conducted from December 24, 2024 through February 3, 2025. Respondents represented Australia, Belgium, Brazil, Canada, Denmark, Finland, France, Germany, India, Indonesia, Ireland, Israel, Italy, Japan, Mexico, the Netherlands, New Zealand, Norway, Portugal, Singapore, South Korea, Spain, Sweden, Switzerland, Thailand, the United Arab Emirates, the United Kingdom, and the United States.
