Top Travel & Hospitality Tech Firms

Monday, August 24, 2026

The movement of business information across cloud platforms, internal systems, applications, and devices has made protecting that data a more complex operational task. Security teams must be able to track the locations of sensitive data, who has access to it, and how it flows through an organization. Manual checkings can be tricky to keep up with, especially as the amount of data increases. Some automated security controls can take care of mundane monitoring, access controls and policy enforcement, while allowing for experts to dedicate more time to investigating security incidents. Automated data security solutions are thus playing a more crucial role in effective data protection strategies, especially for businesses using sensitive data like that related to customers, finances and operations. Market Shifts in Automated Data Protection The market is progressing towards security platforms that can secure information across multiple environments and not just one repository at a time. Cloud storage, databases, applications and endpoints are frequently deployed under varying configurations, leading to the creation of varied and difficult-to-manage security controls. Automated discovery tools can search through data stores and mark and organize records based on profiles. Improved classification enables more effective controls to be implemented at higher risk locations, and less significant restrictions to be imposed on routine business information. Access control systems are also getting smarter. Static permissions can continue to be in effect when employees move to a different role or when they no longer need to access certain information. Automated systems can audit user roles and access activity, and modify permissions based on set policies. Behavioral monitoring takes it further by spotting unusual logon times or file access, which can indicate unusual activity. These controls can enable organizations to keep access at the right level without the security teams having to go through each change in permissions. Data loss prevention is increasingly linked to automated monitoring. Security systems can monitor data transfers over email, cloud storage and endpoints and compare activity to organizational policies. If a transfer is made that conveys sensitive information, it can be subject to further verification, it can limit movement, or be the subject of an alert for review. Sensitive data can also be automatically encrypted, ensuring it is protected properly at all times, in all places, and across all storage types. With distributed data, consistency can be maintained with centralized policy enforcement. Security Challenges and Practical Solutions Security alerts can cause stress for security teams, and it may be harder to identify important events when there are a lot of alerts. An automated risk scoring offers a practical answer and assesses events based on data sensitivity, access history and user behavior. Regular activities may continue following established rules, but those with higher risk levels may be subject to investigation. In cases where there is not enough context in the message or an automated response may impact legitimate business activity, human review can still be valuable. Secure information across hybrid environments can also provide management challenges. Cloud platforms and internal systems may have different configurations and security policies. A centralized policy management approach can give a single policy framework, and then individual controls can take into account the technical requirements of the individual environments. Gradual integration is helpful when the legacy systems are not capable of supporting modern security interfaces. Gateways and monitoring layers can be used to gain visibility without the need to replace existing applications in one fell swoop. Governance of automation needs to be done with care, as badly configured automation can lead to operational disruption. It is possible to discover surprising effects when testing security policies in controlled environments prior to deployment. Clear response thresholds can also be used to differentiate between what needs to be done automatically and what needs to be approved. Expensive decisions, such as blocking critical access to business, can still be subject to human authorization. Advancing Capabilities and Stakeholder Value Machine learning is being used to help detect patterns of behavior that may not be defined in rules to protect against security threats. Access paths that do not follow the norm, or transfers or changes in system activity, can be assessed against defined behaviors. The best use cases are those that use statistics in conjunction with traditional security measures, not exclusively relying on automated predictions. Even in the face of uncertainty, sound information and good judgment are crucial in determining the way the organization should act. Security orchestration is also helping to improve the way that various controls interact. When one system identifies a suspicious event, it can trigger a series of events across various aspects of identity management, endpoint protection and access control. A coordinated response can help minimize the time between detection and containment, as well as repetitive work by the security team. It also establishes uniformity in the response process for various types of incidents. The other functional advantage is automated compliance monitoring. Access permissions and configurations, as well as data locations, can be continually validated against internal policies or regulatory mandates by security platforms. Automated records enable compliance teams to pinpoint areas in need of attention and provide better evidence of the effectiveness of controls. Continuous monitoring can also help to minimize the need for periodic manual monitoring.

Monday, August 24, 2026

AI Readiness Score of 16 Out of 100 Exposes a Critical Controls Gap San Mateo, California | July 30, 2026 — Kiteworks, which empowers organizations to effectively manage risk in every send, share, receive, and use of private data, today released its 2026 Data Security and Compliance Risk: Annual Survey Report, based on primary research with security, compliance, risk, and IT professionals across 10 industries and three global regions. Now in its fifth year, the finding is stark. Eighty percent of organizations experienced at least one security or AI-related incident in the past 12 months, not a projected risk but a reported outcome. The incidents came with consequences. Sixty-three percent of organizations experienced a compliance outcome, such as an audit finding, a required remediation plan, a board escalation, a contractual penalty, or a formal regulatory investigation. And 65% discovered employees using unapproved AI tools with sensitive organizational data. What sets this report apart is how the numbers are derived. Rather than self-reported confidence, it scores organizations on deployed controls, what is technically operational. The Data Security Maturity Score (DSMS) evaluates 11 binary security controls, 8 general ones protecting everyday human-driven data access and 3 AI-specific. The AI Governance Maturity Score (AIGMS) measures 19 AI and agent governance capabilities. Neither can be inflated by perception. The results are stark. It found a mean DSMS of 39 out of 100, a mean AIGMS of 35 out of 100, and a combined Data Security and Compliance Readiness Index (DSCRI) of 16.2 out of 100. Seventy percent of organizations sit in Tier 1 or Tier 2, developing maturity at best. Read together, the two scores show whether controls are keeping pace with both people and agents. “Organizations have deployed AI far faster than they’ve built the governance infrastructure to manage it,” said Tim Freestone, Chief Strategy Officer at Kiteworks. “The incidents have already happened, and the compliance consequences are already being felt. The path forward is architectural. It requires a data policy engine that enforces controls at the data layer for every person and every agent alike, not behavioral policy people can route around. Organizations that reached Tier 4 maturity got there by deploying controls, not documenting intent.” The governance gap is measurable. No AI containment control in the survey is deployed by more than 31% of organizations. Fifty percent cannot produce a complete AI data access audit record within one business day, a direct exposure under DORA, NIS2, and the EU AI Act. Seventy-three percent have no technical enforcement over which channels employees can use for sensitive data, and only 27% have deployed AI-specific DLP. The shadow AI problem is equally structural. Among organizations that discovered employees using unapproved AI tools, 36% found customer and client data flowing through them, 33% found IT credentials, and 31% found employee personal and HR data. The 35% reporting no discovery likely lack the detection capability to see it. “AI risk is no longer a future problem. It is a present condition most organizations are still treating as a planning exercise,” said Patrick Spencer, SVP of Americas Marketing and Industry Research at Kiteworks. “Organizations still waiting to act are behind, not ahead. This research gives leaders defensible data grounded in deployed controls, not stated intentions, the foundation for moving investment toward architecture that governs people and agents under one standard.” The gap between the top and bottom of this survey is 38 DSCRI points. The 19% of organizations in the Resilient quadrant (DSMS and AIGMS both at least 50) carry a mean DSCRI of 46. The 66% in the Exposed quadrant (both below 50) carry a mean DSCRI of 8, despite similar industry and size mixes. The difference is deployed AI governance controls. At the survey mean DSMS of 39, raising AIGMS from 35 to 60 adds roughly 10 DSCRI points, nearly double the gain from adding four security controls while AIGMS stays fixed. The report identifies seven priorities for closing that gap. • Classify and enforce sensitive data • Deploy AI-specific DLP through a centralized policy engine • Integrate MFT and AI infrastructure with a SIEM • Implement and test an AI kill switch • Build audit trails that meet regulatory production timelines • Assign dedicated AI data governance ownership • Consolidate sensitive data exchange platforms The full report includes industry, regional, and organization-size breakdowns, and a Security Maturity Readiness Checklist for prioritizing remediation investment. Organizations can benchmark their own readiness using the Kiteworks AI Data Governance Readiness Assessment, an online tool that scores controls across the same dimensions measured in this report. Access it at [URL forthcoming]. Download the 2026 Data Security and Compliance Risk: Annual Survey Report at https://www.kiteworks.com/sites/default/files/resources/kiteworks-annual-report-2026.pdf The research was conducted by Centiment on behalf of Kiteworks in Q2 2026.

Friday, August 21, 2026

Fremont, CA: Modern businesses are increasingly relying on digital platforms to manage facilities and operational control with greater flexibility, efficiency, and scalability. Traditional management systems often operate in isolated environments, making it difficult for organizations to maintain real-time visibility across assets, teams, and processes. Advanced platforms are changing this approach by centralizing operations, automating workflows, and enabling continuous monitoring from virtually any location. As facilities become more connected and operational demands grow more complex, organizations are adopting intelligent management platforms that integrate data, communication, and control systems into a unified ecosystem. The solutions support smoother coordination across departments while enabling businesses to respond faster to operational challenges. The result is a more agile and responsive operational environment capable of scaling with business growth. User-friendly interfaces and mobile accessibility are further improving adoption. By leveraging advanced operational platforms, organizations can manage facilities with greater intelligence, flexibility, and efficiency. Through centralized control, automation, predictive analytics, and integrated systems, businesses are removing operational limitations and creating smarter, more scalable environments for long-term growth. How Can Centralized Control Enhance Real-Time Visibility? Businesses can manage building systems, maintenance, energy usage, workforce coordination and asset performance through a unified digital interface. OptiCloud uses intelligent automation and real-time insights to improve efficiency across cloud and AI environments. Centralized access reduces fragmented processes, while continuous operational data analysis helps managers monitor performance and identify potential issues earlier. Immediate access to insights improves responsiveness and supports faster decision-making during critical situations. Remote accessibility is reshaping operational management. Cloud-based platforms allow teams to oversee facilities, monitor equipment, and manage workflows from virtually anywhere. The flexibility supports modern work environments and ensures uninterrupted operational oversight across geographically distributed locations. DigitalNet.ai integrates cognitive agents with enterprise workflows to analyze operational data and support more consistent decision-making. How Can Predictive Intelligence Transform Integrated Ecosystems? Organizations can expand facilities, add new systems, and integrate additional functions without completely restructuring existing infrastructure. The adaptability allows businesses to grow efficiently while maintaining centralized operational control. Predictive intelligence is advancing operational management significantly. AI-driven analytics and smart monitoring systems can identify patterns, forecast maintenance needs, and detect operational inefficiencies before they impact productivity. Integrated ecosystems are becoming increasingly common as businesses seek seamless connectivity between systems. Modern platforms can connect HVAC systems, security controls, energy management tools, IoT devices, and workforce applications into a synchronized operational environment. The integration improves coordination and enables smarter facility management strategies. Employees and managers can access dashboards, alerts, and operational insights quickly through intuitive applications, improving efficiency and collaboration across teams. 

Thursday, August 20, 2026

Enterprise translation buying has become harder because easy work is getting cheaper while costly mistakes remain costly. AI can push large content volumes through multilingual workflows, but buyers in regulated, technical, clinical and customer-facing environments still carry the burden of accuracy, terminology control, approval timing and cultural fit. The sourcing question is no longer language count alone. Procurement teams have to ask where automation belongs, where expert review remains nonnegotiable and how the supplier proves that judgment before content reaches customers, regulators, field teams and internal users. The pressure is uneven across the enterprise. Marketing teams may need voice adaptation across markets. Legal and intellectual property groups need precise language tied to filing requirements and claim scope. Life sciences teams face documentation where a small error can delay approval or create avoidable review cycles. The stronger model is not a generic AI layer wrapped around translation. It is a service structure that changes by content type, buyer function, language pair and tolerance for error. AI has made that distinction more visible. General models are useful on repeatable or lower-risk content, but enterprise translation depends on memory systems, terminology discipline, workflow testing and human review rules. A model that performs well in one language may be weak in another. A prompt approach that works for support content may not suit clinical, patent, legal and technical material. Executive buyers should look for evidence that a provider tests AI in near-production settings before scaling it, using benchmarking by content type, controlled pilots, error detection routines and a clear path from test to approved use. Service design matters as much as model choice. Translation and localization are bought by different functions inside the same global enterprise, and those functions rarely share the same risk profile. A provider built around customer and content specialization is better placed to learn the buyer’s vocabulary, regulatory context, review habits and release cadence. It can also extend beyond translation when the work demands adjacent execution, like patent filing support or data preparation for AI systems. That fit is harder to assess from language coverage alone. It shows up in workflow ownership and the ability to know when speed should yield to control. Internal AI adoption also deserves scrutiny. Many language suppliers can describe AI tools, but fewer have changed how work gets planned and tested. Buyers should favor firms that give staff secure AI access and formalize repeatable use cases. Experimentation without guardrails can become risk. Guardrails without experimentation can leave cost and speed advantages unused. The practical middle ground is disciplined testing and a willingness to retire older workflow assumptions when the evidence supports it. That buying logic makes Welo Global the premier choice for enterprises that need business translation and localization tied to complex content rather than generic language output. Its business structure separates localization, life sciences, AI data and patent-filing work, allowing methods to shift by buyer group and content risk. Its AI work is grounded in testing, benchmarking, specialist review and post-editing rules rather than simple automation claims. For executives balancing scale with review discipline, Welo Global offers a strong fit because it treats localization as specialized enterprise work, not a volume exercise.