OneTrust Unveils New Capabilities to Discover, Secure, and Responsibly Use Data
CIO Applications | Thursday, May 23, 2024
AUSTIN, Texas, : OneTrust, the market-defining leader for trust intelligence, today announced new platform capabilities and enhancements to help organizations discover, secure, and responsibly use data. Available as part of the Company's latest release, these innovations empower organizations to activate data responsibly, surface and mitigate risk, and navigate the complex regulatory environment.
"We are at an inflection point where managing your data responsibly has never been more complex or more mission-critical," said Blake Brannon, Chief Product & Strategy Officer at OneTrust. "To grow and thrive in this data-centric era, organizations need a foundation to discover, secure, and responsibly use data – across the entire enterprise, throughout the entire data lifecycle, and with context from all risk angles. The breakthrough innovations we're unveiling at TrustWeek enable our customers to innovate at the speed needed in this era of AI and data consumption, all while improving compliance and mitigating risks."
Activate data responsibly and govern the use of AI without slowing down innovation. Gain data visibility and enforce policies and controls, while simplifying responsible first-party data capture.
• OneTrust Data Discovery makes it easier than ever to uncover potential security and compliance issues, automatically apply the right policies, and reduce data risk. An enhanced Data Classification Engine simplifies configuration and customization for structured and unstructured data, offering more than 300 out-of-the box classifiers for improved accuracy. AWS Automated Deployment simplifies provisioning and management of Data Discovery on AWS infrastructure, reducing deployment time by more than 50%.
• An enhanced Data Policy Engine helps strengthen data security and compliance posture. Rules-based data policies automatically flag violations based on data discovery scans, giving visibility into data risk that should be remediated. Duplicate File Detection automatically identifies duplicate data to reduce redundant data and decrease the attack surface, improve security posture, and potentially minimize cloud storage costs.
• Advancements to OneTrust AI Governance help govern the use of AI without slowing down innovation. A new AI Inventory Graph and Enhanced Data Graph visualize the components of the AI systems in use so users can understand dependencies. Enhanced AI Governance Automation Rules enables users to tailor workflow rules to their specific requirements without developer help. AI Regulatory Research now offers an in-app dashboard to provide real-time updates on AI regulations and laws.
• OneTrust Cookie Consent is bolstered by an enhanced Website Scanning Engine to surface all trackers on a website, mitigate the risk of non-compliance, and reduce customer scan times. AI-assisted Cookie Identification and Categorization significantly decreases the number of unidentified cookies per scan to minimize manual categorization.
Surface and mitigate risk across your data, third parties, or compliance requirements. Streamline risk management across the enterprise, identify and reduce compliance-related risk, and scale your third-party management program.
• OneTrust IT & Security Risk Management now offers Dynamic Risk Workflows to reduce time spent manually reviewing risk from one workflow stage to the next, enforcing better data hygiene.
• Third-Party Risk Management allows users to streamline vendor evaluations and AI Governance with Third-Party AI Usage Tracking. Teams can ensure effective risk management and secure vendor AI integration. Initiate AI risk management workflows directly from within vendor assessments and sync information across inventories.
• Third-Party Due Diligence now makes it easier to keep your third-party ecosystem in check. When a third party no longer needs oversight, Termination of Monitoring stops the daily monitoring of third parties and linked entities on a case-by-case basis or by using workflow rules.
• OneTrust Compliance Automation tackles the challenges of a sprawling regulatory landscape by empowering companies to centrally manage and automate compliance initiatives across regulations, standards, and frameworks. Now, users can connect to external systems, automatically collect evidence, and map it to controls with Automated Evidence Collection. Enterprise and Inventory-specific Controls help eliminate redundant control records and drive consistency of controls operating across different parts of the business. Control Objectives make outcomes more manageable, allowing teams to define and categorize controls by a common desired outcome and analyze controls more efficiently with aggregated insights.
• Ethics Program Management now offers Data Redaction for Disclosures to redact personal data from disclosure forms, providing an additional layer of privacy and security to the disclosure process and protecting data in case of a leak or breach.
Work smarter, together to navigate the complex regulatory environment. Empower your team with AI and regulatory intelligence to simplify compliance and free up resources.
• The largest regulatory research platform in the world, OneTrust DataGuidance, is now redesigned to make it easier to use, faster to navigate, and ready to deliver smarter insights. AI-powered navigation with a new built-in OneTrust Copilot makes it easier than ever to get the regulatory answers you need. Plus, users have regulatory content tailored to their profile.
• Automated Sandbox Creation enables users to optimize configurations and reduce the risk of issues by creating and maintaining their own sandbox tenants that can be tested independently of the production environment.
"We are at an inflection point where managing your data responsibly has never been more complex or more mission-critical," said Blake Brannon, Chief Product & Strategy Officer at OneTrust. "To grow and thrive in this data-centric era, organizations need a foundation to discover, secure, and responsibly use data – across the entire enterprise, throughout the entire data lifecycle, and with context from all risk angles. The breakthrough innovations we're unveiling at TrustWeek enable our customers to innovate at the speed needed in this era of AI and data consumption, all while improving compliance and mitigating risks."
Activate data responsibly and govern the use of AI without slowing down innovation. Gain data visibility and enforce policies and controls, while simplifying responsible first-party data capture.
• OneTrust Data Discovery makes it easier than ever to uncover potential security and compliance issues, automatically apply the right policies, and reduce data risk. An enhanced Data Classification Engine simplifies configuration and customization for structured and unstructured data, offering more than 300 out-of-the box classifiers for improved accuracy. AWS Automated Deployment simplifies provisioning and management of Data Discovery on AWS infrastructure, reducing deployment time by more than 50%.
• An enhanced Data Policy Engine helps strengthen data security and compliance posture. Rules-based data policies automatically flag violations based on data discovery scans, giving visibility into data risk that should be remediated. Duplicate File Detection automatically identifies duplicate data to reduce redundant data and decrease the attack surface, improve security posture, and potentially minimize cloud storage costs.
• Advancements to OneTrust AI Governance help govern the use of AI without slowing down innovation. A new AI Inventory Graph and Enhanced Data Graph visualize the components of the AI systems in use so users can understand dependencies. Enhanced AI Governance Automation Rules enables users to tailor workflow rules to their specific requirements without developer help. AI Regulatory Research now offers an in-app dashboard to provide real-time updates on AI regulations and laws.
• OneTrust Cookie Consent is bolstered by an enhanced Website Scanning Engine to surface all trackers on a website, mitigate the risk of non-compliance, and reduce customer scan times. AI-assisted Cookie Identification and Categorization significantly decreases the number of unidentified cookies per scan to minimize manual categorization.
Surface and mitigate risk across your data, third parties, or compliance requirements. Streamline risk management across the enterprise, identify and reduce compliance-related risk, and scale your third-party management program.
• OneTrust IT & Security Risk Management now offers Dynamic Risk Workflows to reduce time spent manually reviewing risk from one workflow stage to the next, enforcing better data hygiene.
• Third-Party Risk Management allows users to streamline vendor evaluations and AI Governance with Third-Party AI Usage Tracking. Teams can ensure effective risk management and secure vendor AI integration. Initiate AI risk management workflows directly from within vendor assessments and sync information across inventories.
• Third-Party Due Diligence now makes it easier to keep your third-party ecosystem in check. When a third party no longer needs oversight, Termination of Monitoring stops the daily monitoring of third parties and linked entities on a case-by-case basis or by using workflow rules.
• OneTrust Compliance Automation tackles the challenges of a sprawling regulatory landscape by empowering companies to centrally manage and automate compliance initiatives across regulations, standards, and frameworks. Now, users can connect to external systems, automatically collect evidence, and map it to controls with Automated Evidence Collection. Enterprise and Inventory-specific Controls help eliminate redundant control records and drive consistency of controls operating across different parts of the business. Control Objectives make outcomes more manageable, allowing teams to define and categorize controls by a common desired outcome and analyze controls more efficiently with aggregated insights.
• Ethics Program Management now offers Data Redaction for Disclosures to redact personal data from disclosure forms, providing an additional layer of privacy and security to the disclosure process and protecting data in case of a leak or breach.
Work smarter, together to navigate the complex regulatory environment. Empower your team with AI and regulatory intelligence to simplify compliance and free up resources.
• The largest regulatory research platform in the world, OneTrust DataGuidance, is now redesigned to make it easier to use, faster to navigate, and ready to deliver smarter insights. AI-powered navigation with a new built-in OneTrust Copilot makes it easier than ever to get the regulatory answers you need. Plus, users have regulatory content tailored to their profile.
• Automated Sandbox Creation enables users to optimize configurations and reduce the risk of issues by creating and maintaining their own sandbox tenants that can be tested independently of the production environment.
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Fremont, CA: Artificial Intelligence (AI) is revolutionizing the construction sector. There has never been a greater pressing demand for accuracy and efficiency due to tighter budgets and more complicated projects. Here are five domains in construction project management where AI plays a crucial role.
Decision-Making
The use of AI in decision-making has been one of its most revolutionary effects. Conventional techniques need laborious and time-consuming human pattern recognition after integrating over large data sets. Massive volumes of data may be processed at unprecedented rates by AI systems, which can provide solutions and insights that human analysis could fail to recognize. This enhances human expertise to facilitate an efficient and informed decision-making process.
Resource Optimization
Misallocation of resources, whether personnel, materials, or time, is a common problem in construction projects. Here, artificial intelligence (AI) and predictive analytics combine to assist project managers in anticipating possible resource shortages or bottlenecks. Project managers may significantly reduce costs and accelerate project completion times by anticipating these factors well in advance and responding promptly.
Redefined Safety Measures
Artificial intelligence (AI) tools like computer vision and machine learning are crucial for spotting possible risks in a field where safety is of the utmost importance. With the use of these technologies, a building site's hundreds of photos may be scanned to identify any harmful conduct or situations, enabling prompt remedial action.
Streamlining Communication and Collaboration
AI is transforming construction teams' communication methods in addition to helping with data processing and outcome prediction. Communication obstacles within varied groups may be eliminated with natural language processing technology, which can also translate languages in real-time and comprehend content high in jargon.
Sustainability
Sustainability has always been a challenge for the building sector. Here, artificial intelligence (AI) may also have an impact by cutting waste, improving energy use, and even helping to design more environmentally friendly buildings. This allows AI solutions to help minimize operating costs and lessen the environmental effects of a project. ...Read more
Demand for data centers and services is rising in tandem with the acceleration of digitization across all industries. Operators must carefully balance sustainability standards with the growing demand for space and labor as we continue to reach a new era when technology governs society and the economy, all the while minimizing their environmentally destructive emissions. Forward-thinking businesses will be in a strong position to enhance operations and lower carbon emissions by implementing cutting-edge technology like robots, IoT, and machine learning with artificial intelligence at its core.
Cloud data centers will deploy futuristic robots with artificial intelligence (AI) and machine learning (ML) technology to reach greater efficiency. Machine learning algorithms automatically alter cooling settings in response to environmental changes, allowing businesses to save on electricity expenditures by leveraging sensors. Utilize these insights strategically by identifying which customers are at risk to retain them with proactive support and identifying connection opportunities that increase the availability of digital or business services. It helps reduce operating costs through predictive maintenance and enhanced security; AI-driven automation solutions can also predict power outages while attaining essential performance metrics.
Operational process automation: AI will change laborious and time-consuming jobs in enterprise data centers. Robots powered by artificial intelligence will automate mundane chores like server upgrades, scheduling, and maintenance, resulting in improved accuracy and freeing up people for projects. Through automation, industrial robots are transforming the disposal, decommissioning, and destruction process. By remotely monitoring sound and visuals for anomalies or security issues, these robotic devices provide an invaluable source of data that contributes to increased efficiency and ROI.
Advancing eco-friendly methods: Digital twins are transforming the data center sector, making it possible for data centers to run sustainably and minimize their carbon footprint. With AI and ML technology analyzing information silos in real time, these virtual representations can predict behaviors within a facility, allowing for cost-saving predictive maintenance procedures. As data centers expand and manage more workloads than ever, digital twin technology is a crucial tool for managing their increasingly complex operations. Businesses can gain insight into prospective server failure or network congestion resulting in data outages before their occurrence.
Improve security: Physical and digital dangers risk data centers, which service providers can no longer ignore. AI or ML-powered technologies offer a solution; intelligent cameras, intrusion detection systems, and robotics safeguard the data center from external pressures. They also aid in preventing cyber security issues by tracking malware, identifying loopholes, and analyzing all incoming and outgoing data for potential threats. Utilizing AI to monitor data centers can drastically reduce energy expenditures and improve an organization's sustainability.
Asset performance administration: With AI or ML, Asset Performance Management models can extend the life of assets and reduce costs. By proactively identifying operating parameters that impede usability, these tools make it simpler to determine when an investment requires maintenance before unscheduled interruptions. Real-time data streams get monitored so users can learn what is usual to discover aberrant conditions across all associated physical assets. ...Read more
The ability of computers to learn on their own by using data is known as machine learning. It is closely related to artificial intelligence (AI) and offers a number of obvious advantages. Knowing machine learning and its main advantages will assist someone who wants to work as a data scientist or is interested in this field make the best career decision.
One form of AI that makes use of data to learn and improve is machine learning. Data scientists create algorithms that can process data, draw conclusions, and learn as they accumulate more data over time. Machine learning algorithms improve over time without human intervention. There are numerous types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning is used to make predictions, and the system learns from training data. Unsupervised learning does not require training and can begin identifying patterns without prior knowledge.
Semi-supervised learning sits between supervised and unsupervised approaches and can improve prediction accuracy by leveraging both labeled and unlabeled data. Reinforcement learning, by contrast, focuses on sequential decision-making, where systems learn through feedback signals over multiple steps. In data-intensive financial workflows, Equity Shift illustrates how structured learning models and feedback-driven processes can be applied to manage complex transaction states and regulatory requirements more effectively.
Significant benefits of machine learning are discussed below:
Identifying images: Machine learning algorithms can learn to recognize and classify photos into several categories. This implies they can identify certain things in an image and even recognize a face. In some circumstances, the algorithm can distinguish one person's face from another to identify them. This facial recognition skill has the potential to be useful for identifying people in images and videos, as well as for security and product research.
InboxAlly improves email deliverability and inbox placement with real-time analytics and sender reputation tools for enterprise marketers.
Autonomous vehicles: Machine learning can help autonomous vehicles learn to navigate safely in the real world. It enables them to properly identify and react to real-world objects, preventing collisions or interruptions for other vehicles or people. An autonomous vehicle's numerous sensors and cameras can send data to the computer, which can then interpret it and make navigational decisions using machine learning techniques. Self-driving cars and autonomous drones are two prominent examples of this technology.
Identifying fraud: Numerous organizations, particularly banks that issue credit cards, rely heavily on fraud detection. Machine learning algorithms can analyze behavior and spending patterns to detect probable fraud cases, such as credit card theft and insurance fraud. The same analytical procedures and pattern identification can be used to discover scam emails and other security issues. ...Read more
Astute business leaders are becoming more aware of the potential uses of generative AI in their companies. Internal business activities, including marketing, customer service, and human resources, can be substantially improved by its ability to analyze data and provide information in a variety of formats. But what effects does this have on the supply chain, one would wonder?
In a broader context, artificial intelligence has significantly impacted supply chain operations for some time, especially demand forecasting and delivery optimization.
What is the Potential of Generative AI?
Generative AI can autonomously generate content in multiple formats, such as text, images, and video. Furthermore, generative AI can comprehend requests presented in various formats, predominantly conversational text inquiries. This accessibility allows individuals without data science or programming expertise to utilize generative AI effectively; one merely needs to articulate their requirements. Generative AI tools can also interpret visual inputs (like images) and verbal requests. Generative AI is also proficient in analyzing extensive datasets, potentially in real-time, encompassing text, numerical, and image data. Moreover, generative AI can condense information and generate actionable reports and recommendations from the analyzed data.
Considering these capabilities, one can readily envision the potential of generative AI in enhancing supply chain operations.
Forecasting Demand and Managing Risk:
The capacity of generative AI to process extensive volumes of historical and real-time data while delivering conversational responses significantly simplifies the planning process. Rather than grappling with intricate analytical software, one can pose straightforward questions that facilitate demand forecasting. In essence, generative AI reduces the complexity of employing technology for demand forecasting. Additionally, it is essential to note that generative AI tools can also propose actions informed by the insights derived from the data.
Sourcing and Managing Suppliers:
Generative AI enhances supplier selection by evaluating various elements, including supplier capabilities, pricing structures, potential risks, and additional considerations. Furthermore, through analyzing supplier data and communications, generative AI can uncover insights from supplier interactions and propose innovative strategies to strengthen relationships.
Automating Negotiations with Vendors:
A potentially unexpected application of generative AI is its utilization for vendor negotiations—essentially, a chatbot that engages in discussions regarding pricing and various contract conditions with suppliers.
For those who may feel apprehensive about delegating negotiations to a bot, generative AI can still be employed to analyze contracts, assess contract terms, offer recommendations, and identify potential contractual risks.
Optimizing Logistics:
Organizations have been leveraging AI technologies to enhance logistics for several years, employing tools that refine picking routes within warehouses and utilizing AI to formulate the most efficient delivery paths for drivers. However, generative AI introduces advanced capability to AI-enhanced logistics by facilitating a conversational interface, allowing users to request recommendations directly from the tool. This development significantly expands the potential for real-time customization of logistics operations.
Enhancing the Production Process:
Generative AI can significantly boost the manufacturing process. Notable examples include using AI-driven design tools to expedite the design phase and implementing predictive maintenance strategies to identify machines or production lines at risk of failure, facilitating prompt maintenance and reducing machine downtime. ...Read more
