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Keeper Security Names Jessica Krowel and Bill Grabner to Lead North American Sector Sales Keeper Security, the leading zero-trust and zero-knowledge identity security platform, announced the appointment of Jessica Krowel as Senior Vice President of North America Sales, Private Sector, and Bill Grabner as Senior Vice President, Public Sector. Both executives will support Tim Strickland, Chief Revenue Officer, as Keeper continues to strengthen and scale its go-to-market leadership. These key talent acquisitions come on the heels of Gartner® recognizing Keeper as the second fastest-growing competitor in its Market Share Analysis: Security Software, Worldwide, 2025 report. New Go-to-Market Leadership Jessica Krowel brings almost 20 years of go-to-market leadership in identity and access management. She spent 14 years at CyberArk, rising through the ranks to Global Vice President of Inside Sales and Chief of Staff to the Go-to-Market (GTM) organization. Most recently, she served as Vice President of Global Sales Development at Delinea. Bill Grabner brings more than two decades of federal and public sector sales leadership. Most recently, he served as Senior Vice President and General Manager at JMA Wireless, with prior leadership roles at Ribbon Communications, Cisco and BroadSoft. ABM Insights: Optimove Announced the Introduction of the New OptiGenie Krowel and Grabner will support and scale Keeper’s GTM teams across key regions to address demand across: Midmarket organizations Large-scale enterprises Federal organizations Accelerating Keeper’s Growth Last year, Keeper grew its revenue 53.4% in 2025, more than three times the security software market’s overall average of 15.5%, ranking second only to Google in Gartner’s analysis. The $111 billion worldwide security software market grew steadily last year, and Gartner attributed the top performers’ growth in part to platform consolidation and organizations replacing fragmented point solutions with unified platforms. “The cybersecurity market is seeing an unprecedented inflection point with the advent and widespread adoption of AI technologies,” said Tim Strickland, Chief Revenue Officer of Keeper Security. “With Jessica and Bill onboard, Keeper is poised to continue accelerating its rocketing growth as organizations seek identity security solutions that harness the power of AI to protect against existing human risks and emerging agentic AI threats.” Read More: https://theabm.info/keeper-security-names-jessica-krowel-and-bill-grabner-to-lead-north-american-sector-sales

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An account's existing verification status does not eliminate the need for secure authentication or continued compliance with applicable platform requirements. Long-term account management should therefore be based on responsible ownership and ongoing security rather than a one-time verification label. Should Agencies Handle Another Person's KYC Documents? Agencies should avoid unnecessary access to another person's sensitive identity information. The phrase "Buy Verified Binance Accounts" can refer to accounts verified using documents belonging to someone else, raising privacy and responsibility concerns. If an agency legitimately manages a client's account, its responsibilities for document handling should be clearly defined, and information should be appropriately protected. Agencies should not assume that another person's identity documents can be used to establish ownership for the agency or another client. Clear ownership and controlled information handling provide a more appropriate foundation for professional account management. What Is the Best KYC Approach for a Crypto Business? A strong approach is to establish the account with accurate information for the actual individual or eligible business entity and then maintain secure records. The keyword Buy Verified Binance Accounts may be associated with faster access, but businesses should prioritize transparent ownership and sustainable management. Companies can prepare documents in advance, use appropriate security controls, define account responsibilities, and monitor applicable requirements. This approach helps ensure that the identity associated with the account corresponds to the organization or person actually responsible for its use, making future verification and account management processes easier to understand. 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NeXasure and Atsign Partner to Strengthen Cyber Resilience NeXasure, a pioneering provider of AI-driven cybersecurity managed detection and response solutions with advisory services, and Atsign, the innovative creator of a privacy-by-design networking platform, announced the completion of bilateral marketing and reseller agreements that will help organizations reduce exposed infrastructure, strengthen cyber resilience, and secure both new and existing connected systems. The partnership brings together two complementary technology philosophies: NeXasure’s proactive, agentic AI approach to cyber resilience, which uses a true agentic AI “brain” with a dynamic ontology that accurately detects and remediates cyber threats in seconds instead of hours to days. The Atsign platform, the cryptographic identity and secure communications foundation behind NoPorts and Atsign-enabled applications. The Atsign platform enables the development of secure connected systems without exposed infrastructure. Infotech Insights: Cahill Names Kelly Graves Chief AI Strategy and Innovation Officer Combining AI-Driven Defense With Privacy-First Networking NeXasure Defend leverages Frostbow from SKADI Cyber Defense, a synthetic and agentic AI solution deployed to detect, analyze, and respond to threats in real time. Atsign eliminates entire classes of attack vectors by ensuring that no data is exposed without explicit permission. The platform removes reliance on: Open ports Static IP addresses Vulnerable network configurations Together, the combined solution portfolio offers organizations a comprehensive, privacy-first cybersecurity posture, spanning access, network architecture, and active threat mitigation end-to-end. “This is a significant step forward in our mission to deliver holistic, AI-powered cybersecurity solutions,” said Rick McElroy, Chief Executive Officer of NeXasure. “Atsign solutions eliminate the foundational vulnerabilities that attackers routinely exploit — open ports, exposed services, and discoverable infrastructure. Combined with NeXasure Defend’s real-time AI threat intelligence and our Advisory Services team, we are giving our clients an unprecedented level of protection that is both proactive and architecturally sound.” Read More: https://theinfotech.info/nexasure-and-atsign-partner-to-strengthen-cyber-resilience

CyberCube Creates New Framework for Evaluating AI-Driven Losses CyberCube, the leading provider of cyber risk analytics, unveils six new AI Event Families and a new I2T2 framework, providing carriers, brokers, and risk managers with a practical way to think about and evaluate AI-driven loss. The I2T2 framework addresses AI risk in a way that remains applicable to cyber insurance, while also reflecting the wider array of insurance risks now possible. The framework sets out four dimensions through which AI affects insurance risk: Information, Intelligence, Tactics, and Technology. CyberCube’s framework is set out in “Machine State of Mind: A Framework for Quantifying AI-Driven Insurance Risk,” examining how artificial intelligence is reshaping risk across the insurance industry. It notes that AI is no longer a niche technical risk confined to a single line of insurance. As AI systems take on greater autonomy in decision-making, content generation, and operational processes, they introduce new forms of loss that cut across established coverage lines, including, but not limited to, Errors & Omissions (E&O), Technology E&O, Cyber, General Liability, and Directors & Officers, as well as introducing novel impacts that insurance has never covered before. Infotech Insights: Castrol ON PG25 Cooling Fluids Validated for NVIDIA AI Infrastructure As this new intelligence evolves, CyberCube argues that the industry's ways of thinking about insurance risk must evolve with it. Rather than treating AI risk as just an extension of Cyber risk, the framework examines where AI and Cyber overlap, where they diverge, and what this means for how claims are likely to be triggered and allocated across policies. Key Findings AI risk shares several characteristics with Cyber risk, including single points of failure (SPoFs), human error, data loss, and the potential to amplify existing threats. However, it also differs in important ways. For example, AI can cause harm through hallucination or flawed output even when functioning exactly as designed, without the involvement of any malicious actor. AI is best understood as a technology and information revolution, rather than merely an evolution of Cyber risk. As the use of AI extends into many corners of the global economy, new frameworks are required for understanding how AI could affect property-casualty insurance coverages. (Re)insurers grappling with AI risk will follow a path similar to that previously taken with non-affirmative (“silent”) Cyber risk. Lessons from the last decade suggest that the exact application of coverages will vary by carrier. Given the breadth of transformation AI is creating, however, (re)insurers must reckon with AI risk as a multi-line challenge and opportunity. Read More: https://theinfotech.info/cybercube-creates-new-framework-for-evaluating-ai-driven-losses

Healthcare Cybersecurity: Protecting Patient Data and Critical Medical Systems Healthcare cybersecurity has become a critical priority as hospitals, clinics, health technology companies, and medical institutions increasingly depend on digital systems. Electronic health records, connected medical devices, cloud platforms, telemedicine services, and healthcare applications have improved patient care and operational efficiency. However, they have also created a larger attack surface for cybercriminals. Healthcare organizations hold highly sensitive information, including patient identities, medical histories, insurance details, financial records, and clinical data. A successful cyberattack can expose this information while also disrupting essential healthcare services. Growing Cybersecurity Risks in Healthcare Healthcare organizations face a wide range of cybersecurity threats. Ransomware remains one of the most significant risks because attackers can encrypt critical systems and demand payment to restore access. When hospitals cannot access patient records, scheduling systems, or diagnostic information, even routine medical operations can be affected. Phishing and social engineering attacks are also common. Cybercriminals may target employees with convincing emails designed to steal passwords or obtain access to internal systems. Because healthcare environments involve large workforces and sensitive information, a compromised employee account can provide attackers with an entry point into broader networks. Connected medical devices introduce another challenge. Modern hospitals increasingly use network-connected monitors, imaging equipment, infusion pumps, and other devices. If these systems are poorly secured or remain unpatched, attackers may exploit them as potential entry points. Protecting Patient Information Protecting patient data requires more than traditional antivirus software. Healthcare organizations need a comprehensive cybersecurity strategy that combines technology, employee awareness, policies, and continuous monitoring. Encryption is an important component of data protection. Sensitive information should be encrypted both when stored and when transmitted. Strong authentication, including multi-factor authentication, can further reduce the risk of unauthorized access. Organizations should also follow the principle of least privilege. Employees should receive access only to the systems and information necessary for their responsibilities. This limits the potential damage if an account is compromised. Regular security assessments and vulnerability testing can help organizations identify weaknesses before attackers exploit them. Software and medical devices should be patched and updated according to a structured security program. The Role of Artificial Intelligence Artificial intelligence is increasingly being used to strengthen healthcare cybersecurity. AI-powered security platforms can analyze large volumes of network activity and identify unusual patterns that may indicate an attack. Machine learning can help security teams detect suspicious login behavior, abnormal data transfers, malware activity, and other potential threats. AI can also support automated threat detection and response, allowing organizations to react more quickly to security incidents. However, AI also introduces new cybersecurity considerations. Healthcare organizations must ensure that AI systems themselves are properly protected, particularly when they process sensitive patient information. Building a Stronger Security Culture Technology alone cannot eliminate healthcare cybersecurity risks. Employees play an important role in protecting organizational systems. Regular cybersecurity training can help staff recognize phishing attempts, suspicious attachments, weak passwords, and other common attack techniques. Healthcare organizations should also maintain incident response plans. These plans should clearly define how teams will detect, contain, investigate, and recover from cybersecurity incidents. Regular exercises can help organizations identify gaps before a real attack occurs. Looking Ahead As healthcare becomes increasingly connected, cybersecurity will remain a fundamental part of digital healthcare transformation. Organizations must protect not only patient information but also the systems and devices that support clinical operations. A proactive approach combining strong identity controls, encryption, continuous monitoring, secure medical devices, employee training, vulnerability management, and incident response can significantly strengthen healthcare security. Ultimately, healthcare cybersecurity is about more than protecting technology. It is about protecting patient privacy, maintaining trust, and ensuring that critical medical services remain available when patients and healthcare professionals need them most. Read More: https://thehealthco.info/

AI in Wealth Management: Transforming Personalized Investing and Financial Advisory Artificial intelligence (AI) is rapidly changing the wealth management industry by helping financial institutions deliver more personalized advice, automate routine processes, and make better-informed investment decisions. From intelligent portfolio management to AI-powered client communication, wealth managers are increasingly using advanced technologies to improve efficiency while meeting the evolving expectations of investors. What Is AI in Wealth Management? AI in wealth management refers to the use of machine learning, generative AI, natural language processing, predictive analytics, and automation to support investment management and financial advisory services. These technologies can analyze large volumes of financial and client data, identify patterns, generate insights, and assist advisors with recommendations. Unlike traditional wealth management approaches that often rely heavily on manual analysis, AI enables firms to process information continuously and respond more quickly to changing market conditions and client needs. Personalized Investment Strategies One of the most important applications of AI in wealth management is personalization. AI systems can analyze a client's financial goals, investment preferences, risk tolerance, portfolio history, and broader financial situation to help create tailored investment strategies. Predictive analytics can also identify changing client behaviors and potential financial needs. For example, an AI-powered platform may recognize that an investor's risk profile has changed and prompt an advisor to review the portfolio. This level of personalization can help wealth managers move beyond standardized investment products toward more dynamic client strategies. AI-Powered Portfolio Management AI can support portfolio managers by analyzing market trends, economic indicators, company data, and historical performance. Machine learning models can identify relationships and patterns that may be difficult to detect through traditional analysis alone. AI-powered portfolio tools can assist with asset allocation, portfolio monitoring, risk assessment, and rebalancing. Automated systems can continuously evaluate portfolios and alert advisors when an investment strategy may require adjustment. However, AI does not eliminate investment risk. Market conditions can change unexpectedly, and AI-generated predictions should be treated as decision-support tools rather than guarantees of future performance. Improving Advisor Productivity Generative AI is also changing how wealth advisors work. AI assistants can summarize client meetings, prepare reports, analyze documents, draft communications, and retrieve relevant financial information. By automating repetitive administrative activities, advisors can spend more time building client relationships and providing strategic guidance. AI can also help advisors quickly prepare personalized investment updates and explanations for clients. Enhancing Client Experience Modern investors increasingly expect convenient, digital-first financial services. AI-powered chatbots and virtual assistants can provide answers to common questions, explain financial concepts, and support clients outside traditional business hours. Natural language processing allows these systems to understand conversational questions and provide relevant responses. When combined with human advisors, AI can create a hybrid experience that offers both digital convenience and professional expertise. Risk Management and Fraud Detection AI can strengthen risk management by continuously monitoring transactions, portfolios, and account activity. Machine learning models can identify unusual patterns that may indicate fraud, suspicious transactions, or cybersecurity threats. AI can also help wealth management firms assess portfolio exposure and detect potential risks across different asset classes. Early identification can allow organizations to take corrective action more quickly. Challenges of AI Adoption Despite its benefits, AI adoption introduces important challenges. Wealth management firms must address data privacy, cybersecurity, regulatory compliance, model accuracy, and algorithmic bias. Financial institutions also need strong governance frameworks to determine how AI-generated recommendations are reviewed and used. Human oversight remains essential, particularly when AI influences important financial decisions. The Future of AI in Wealth Management AI is expected to become an increasingly important component of wealth management. Future systems may combine real-time financial data, advanced predictive analytics, generative AI, and automated portfolio tools to provide increasingly sophisticated support for investors and advisors. The most successful firms will likely be those that use AI to complement—not replace—human expertise. By combining intelligent automation with trusted financial advice, wealth managers can improve operational efficiency, deliver more personalized experiences, and build stronger long-term relationships with clients. Read More: https://thefintech.info/

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AI Fraud Detection Trends: How Artificial Intelligence Is Transforming Fraud Prevention Fraud is becoming more sophisticated as digital transactions, online banking, e-commerce, and AI-powered services continue to expand. Traditional fraud detection methods that rely heavily on fixed rules and historical patterns are increasingly struggling to identify new and complex threats. In response, organizations are turning to artificial intelligence (AI) and machine learning to detect suspicious activity faster and more accurately. 1. Real-Time Fraud Detection One of the most important AI fraud detection trends is the move toward real-time monitoring. AI systems can analyze transactions, account activity, device information, location signals, and behavioral patterns within milliseconds. Instead of reviewing suspicious activity after a transaction occurs, organizations can identify unusual behavior immediately and trigger additional verification or block potentially fraudulent transactions. 2. Behavioral Analytics AI-powered fraud detection is increasingly focused on understanding how users normally behave. Machine learning models can establish behavioral profiles based on factors such as login patterns, transaction frequency, spending habits, device usage, and geographic activity. When behavior suddenly deviates from an established pattern, the system can assign a risk score and investigate the activity further. This approach can help organizations detect account takeover, payment fraud, and identity-related attacks. 3. Generative AI and Fraud Detection Generative AI is emerging as another important development in fraud prevention. Organizations can use AI to analyze large volumes of unstructured information, identify relationships between seemingly unrelated events, and support fraud investigators with automated insights. Generative AI can also help security teams summarize suspicious cases, identify potential fraud patterns, and prioritize investigations. However, organizations must implement strong governance because fraudsters can also use generative AI to create more convincing scams, phishing messages, and synthetic identities. 4. Synthetic Identity Fraud Detection Synthetic identity fraud is becoming a significant challenge for financial institutions and digital businesses. Criminals can combine real and fabricated information to create identities that appear legitimate. AI can analyze identity attributes, application information, transaction behavior, device signals, and network relationships to identify inconsistencies. Advanced models can detect connections between seemingly unrelated accounts and uncover fraud networks that traditional systems may miss. 5. AI-Powered Identity Verification Identity verification is also becoming more intelligent. AI-based solutions can analyze biometric information, document characteristics, facial patterns, and user behavior to determine whether an individual is genuine. Organizations are increasingly combining multiple signals rather than relying on a single verification method. This can improve fraud prevention while reducing unnecessary friction for legitimate customers. 6. Network and Graph-Based Fraud Detection Fraud rarely happens in isolation. Multiple accounts, devices, payment methods, IP addresses, and transactions may be connected to the same fraud operation. Graph-based AI systems can visualize and analyze these relationships to identify suspicious networks. This approach is particularly valuable for detecting organized fraud, money laundering patterns, account farms, and coordinated attacks. 7. Explainable AI and Fraud Risk Management As AI becomes more important in fraud prevention, organizations also need to understand why systems flag particular transactions. Explainable AI can provide insights into the factors contributing to a fraud decision. This transparency can help fraud analysts investigate cases, improve operational processes, and support compliance requirements. The Future of AI Fraud Detection AI fraud detection is moving toward systems that are faster, more adaptive, and capable of analyzing multiple sources of information simultaneously. Real-time analytics, behavioral intelligence, generative AI, biometric verification, and network analysis will continue shaping fraud prevention strategies. However, AI should not operate without human oversight. The most effective approach combines advanced machine learning with skilled fraud analysts, strong data governance, continuous model monitoring, and effective cybersecurity controls. As fraud tactics continue to evolve, organizations that invest in adaptive AI-powered detection capabilities will be better positioned to protect customers, reduce financial losses, and build trust in an increasingly digital economy. Read More: https://thefintech.info/