New data shows 55.3 million Suno accounts were affected in a breach exposing contact details, purchases, and partial payment card information.
The post New Data Shows Suno Breach Affected 55M Accounts appeared first on TechRepublic.
New data shows 55.3 million Suno accounts were affected in a breach exposing contact details, purchases, and partial payment card information.
The post New Data Shows Suno Breach Affected 55M Accounts appeared first on TechRepublic.
Summary Bullets:
• 8×8 AI Routing identifies the right expert anywhere in an organization that can resolve a customer’s inquiry, not just the contact center.
• While 8×8 AI Routing is marginally better than legacy systems it still merits a try out.
We’ve probably all found ourselves reciting this famous opening line to a classic song when trying to connect with someone in customer support: “Help! I need somebody. Help! Not just anybody. Help! You know I need someone. Help!”
Desperately in need of assistance, eventually you get connected with either a person or an AI agent. You breathe a sigh of relief but then it turns out they are ill-equipped to handle your inquiry. You need help from somebody, but not just anybody. Well, 8×8 claims that its recently introduced ‘8×8 AI Routing’ will take that sad song and make it better.
8×8 AI Routing identifies the right expert for a given interaction anywhere in an organization, not just the contact center. The technology scans three 8×8 platforms, identifying contact center agents on 8×8 Contact Center plus subject matter experts on 8×8 Engage and back-office employees on 8×8 Work. Each interaction is analyzed in real-time across several factors such as transcripts, historical patterns, and sentiment to match the customer with the right resource immediately. 8×8 claims that legacy skills-based routing systems, in contrast to 8×8 AI Routing, are static, relying on skills inventories that are entered manually and often out of date. Furthermore, 8×8 says, those systems often route to whoever is available rather than whoever is necessarily best suited to resolve a specific request.
While 8×8 AI Routing is better than manual, legacy systems the difference is marginal. Identifying the ‘right’ resource hinges on AI conducting an accurate assessment of skills. While AI in general is powerful it is far from perfect. AI is known to produce errors, and the chance that a customer could get routed to the ‘wrong’ resource is not insignificant. While 8×8 AI Routing does allow for a human in the loop, with administrators having the opportunity to provide final sign-off on the skills assessment, such intervention introduces a manual element into the process. And although organizations can roll out first with a small pilot and grow the system as they gain comfort, that adjustment represents yet another layer of manual manipulation. Bottom line, while 8×8 AI Routing is not radically different than legacy systems, it does have the potential to improve an organization’s customer experience and thus merits a test drive.
The post 8×8 AI Routing Takes a Sad Song and Makes It Better appeared first on IT Connection.
© Amelia Brust/Federal News Network
Bitcoin Magazine
Hyperscale Data Buys More Bitcoin, Bridging Holdings to Over $72 million
Hyperscale Data, Inc. has announced that it’s upped its Bitcoin holdings to over 1,000 digital coins.
The New York Stock Exchange-listed company said Tuesday that it had over 1,087.4527 BTC as of Sunday — or $72.4 million based on today’s prices.
The holdings are split across the company’s wholly owned subsidiaries, Sentinum, Inc. and Ault Capital Group, Inc. (ACG). During the week ended July 19, ACG added roughly 51.5 bitcoin through open-market purchases.
The latest disclosure marks a significant acceleration in Hyperscale Data’s accumulation strategy. The AI data center company held just 627.9 BTC in late March 2026 — meaning it has nearly doubled its position, adding about 460 BTC in under four months.
The buildout is part of the company’s goal of establishing a $100 million digital asset treasury and reaching full parity between its Bitcoin holdings and market capitalization. With a market cap of roughly $63 million, that threshold has now been crossed — the company’s bitcoin alone is worth more than the company itself, before counting cash or its operating businesses.
Executive Chairman Milton “Todd” Ault III leaned into that disconnect, stating, “We now hold more than $70 million in Bitcoin.” He argued the market is assigning zero value to the company’s cash, its Michigan data center, and its portfolio of operating businesses, and said Hyperscale will keep executing while highlighting the widening gap between its market capitalization and underlying value.
At the time of writing, GPUS is trading near $0.13 a share.
Strategy Inc. (MSTR) has become the flagship case study in the evolution of Bitcoin treasury strategies in the corporate world.
Under the leadership of Michael Saylor, Strategy shifted from a traditional software business to buying Bitcoin and allowing investors to get exposure to the asset via its shares which trade on the Nasdaq.
This model has inspired other corporations like Hyperscale Data to add the leading cryptocurrency to their treasuries — though Hyperscale’s case is unusual in that its holdings now exceed its entire market cap, a situation more commonly seen in deeply discounted treasury plays.
This post Hyperscale Data Buys More Bitcoin, Bridging Holdings to Over $72 million first appeared on Bitcoin Magazine and is written by Mathew Di Salvo.
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© Getty Images/Urupong
Security Operations Centers (SOCs) are currently confronting scalability challenges on two fronts: structural and cognitive. The day-to-day reality of modern defensive operations is stark: an analyst frequently begins a shift facing a queue deeply saturated with unvetted alerts. To process a single event, the analyst must open the alert, pivot to a secondary console to complete an investigation, manually enrich an IP address, copy a file hash into a third interface, and cross-reference an asset inventory that may not have been updated in months. Following this, they must author and refine queries, waiting for overloaded databases to return historical context.
The actual work of assessing the investigation’s results and moving to decision-making and action has not even begun. This is the administrative burden of the modern SOC. The true threats are not just those that attempt to bypass defenses, but the critical operational hours lost before an active mitigation attempt is even initiated. While analysts are highly trained professionals, the relentless requirement to perform manual data aggregation inevitably leads to exhaustion.
Threat actors operate at machine speed, utilizing automation to pivot laterally across networks in a matter of seconds, frequently disappearing before defensive teams can even log into their terminals. Expecting human defenders to counter automated threat vectors by manually aggregating bad data is an architectural failure.
Every SOC inherits a highly fragmented data ecosystem. Telemetry is continuously generated by diverse sources, including firewalls, cloud workloads, identity providers, endpoint sensors, and legacy systems. This telemetry arrives in disparate dialects, varying formats, and highly inconsistent levels of fidelity. Before AI tools can accurately reason about a potential threat, or an analyst can initiate a logical investigation and run a playbook response, this raw telemetry must be synthesized.
Historically, organizations analysts take on these complex synthesis processes, manually normalizing data points across different vendor schemas. This represents a key misallocation of human intelligence. The asymmetry in modern security operations is not merely a discrepancy in speed; it is an imbalance in how security teams are forced to allocate their finite time. When operators spend the majority of their shifts wrangling data instead of actively investigating threats, the foundation of the SOC itself is inadequate. To achieve defensive velocity, organizations must recognize that fixing the data foundation is the mandatory prerequisite for improving all downstream security functions.
AI Data PipelinesAddressing the upstream data problem requires the implementation of advanced data pipelines capable of resolving enterprise data chaos before it impacts the detection engine. Frameworks such as SentinelOne’s® Singularity AI Data Pipelines serve as this foundational layer, engineered to ingest telemetry from every source and in every format without requiring months-long integration projects or heavy manual engineering.
Modern pipelines utilize AI to normalize raw telemetry into standardized formats, specifically aligning with the Open Cybersecurity Schema Framework (OCSF). This structural alignment transforms fragmented logs into structured data that is immediately actionable. It eliminates the need for analysts to construct complex regular expressions during critical incidents simply to reconcile how two different software vendors format data, such as usernames or a timestamp.
Efficient data ingestion also requires dynamic, in-flight optimization. Not all telemetry possesses the same analytical value, and storing all generated logs in highly indexed, expensive storage tiers is financially and operationally untenable. Data pipelines optimize data streams by filtering out extraneous noise, trimming excess volume, and routing specific logs based on dynamic criteria. High-value security events are routed and indexed for rapid search retrieval, while lower-priority compliance or operational logs are routed to more cost-effective tiered storage. The result is a substantial reduction in infrastructure costs, a higher signal-to-noise ratio, and a structured data foundation that is completely prepared the moment an investigation is required.
When underlying data pipelines automatically enrich that log with identity and asset information, revealing (for example) that a specific financial director’s laptop in a remote office is communicating with a known botnet, the output transitions from a raw data point into a definitive starting point. Crucially, this enrichment occurs systematically before the human operator ever interacts with the alert. Solving this data problem end-to-end is a primary reason SentinelOne was recognized in the IDC MarketScape for AI SIEM.
When a clean, structured data foundation is properly established, the performance of downstream security tools accelerates. Modern detection engines, such as the Singularity AI SIEM, leverage indexless architectures to manage enterprise-scale telemetry. Because the data is normalized and optimized prior to ingestion, these platforms can execute petabyte-scale queries with minimal latency, ensuring investigative results are delivered before the analyst’s attention wanes.
Within this architecture, detection logic is executed continuously against a stream of clean, correlated telemetry. This transforms an ocean of disparate event logs into readable, centralized dashboards that provide immediate situational awareness. The quantitative benefits of this approach are substantial. With AI SIEM, organizations are already executing their queries 70% faster. Adding AI Data Pipelines further augments this workstream, providing cleaner data for AI to run at optimal efficiency. These improvements represent the direct result of ensuring that the data arriving at the SIEM is inherently fit for purpose.
AI SIEM remains a single, comprehensive SKU with customers automatically receiving integrated pipeline functionality for everyday data optimization rather than treating it as a premium add-on. For every unit of paid Data Ingest capacity, customers can process twice that volume through Data Pipelines. A customer with 500 GB/day SIEM entitlement can push 1 TB/day through the pipeline at no additional cost.
The establishment of a structured data pipeline unlocks the capability for true agentic reasoning within the SOC. Unlike traditional rule-based automation, which executes static responses to predefined triggers, technologies like SentinelOne’s Purple AI operate as a dynamic investigative layer.
When an initial alert is generated, an agentic reasoning system does not simply pause and wait for human triage. It autonomously launches an investigation, comprehensively maps the potential blast radius of the incident, and synthesizes a clear, logical recommendation for containment. Then, the analyst logs into the console and is presented with a fully formed situational briefing rather than a blank investigation screen.
More importantly, an agentic AI layer possesses the capacity to evaluate broader adversarial campaigns rather than isolated security events. In isolation, a minor registry key modification, a singular file write, or a brief outbound network connection may not meet the threshold for a critical alert. Legacy security tools often fail to connect these disparate, low-signal events. However, Purple AI can assemble these seemingly unrelated activities into a cohesive narrative, exposing the overarching strategy of the attacker before a major breach occurs.
This level of autonomous intelligence is strictly dependent on the underlying architecture. Advanced AI algorithms cannot derive accurate conclusions from unparsed, low-quality telemetry. The analytical integrity of the agentic layer is entirely contingent on the principle of data quality; systems like Purple AI require clean, structured data to function effectively, avoiding the fundamental issue of “garbage in, garbage out”.
The final component of a modernized, agentic SOC is the deployment of Hyperautomation to execute defensive responses. To counter threats effectively, organizations must deploy automated workflows capable of executing decisions at machine speed. These no-code workflows can be configured to trigger autonomously based on AI triage verdicts, the disclosure of new high-severity vulnerabilities, or specific incoming alerts. By automating the mitigation phase, the SOC evolves from an environment strictly dedicated to passive observation into a dynamic system that actively neutralizes threats.
However, the implementation of automated response mechanisms must be rigorously governed. Executing changes to enterprise infrastructure carries inherent risk. To mitigate this, automated workflows must integrate critical approval steps, ensuring that highly consequential actions are paused until human authorization is provided. The analyst retains the ultimate authority, defining the precise parameters of what processes may run automatically and what workflows require manual judgment.
The strategic objective of integrating data pipelines, agentic reasoning, and Hyperautomation is not the removal of the human operator. Instead, the overarching goal is the restoration of the analyst’s primary function: exercising expert judgment.
By offloading repetitive tasks to technological systems, organizations systematically remove operational friction. The data layer filters out irrelevant noise, allowing the analyst to clearly see the threat. The AI investigation layer removes the administrative grind of data collection, allowing the analyst to focus purely on analytical thinking. Finally, the automated response layer eliminates procedural delays, ensuring the analyst’s decisions are executed rapidly enough to matter. This creates an intelligence fabric, known as Autonomous Security Intelligence (ASI), where data, investigation, and response function concurrently as a single, unified system.
Under this model, the operational output of a single analyst is exponentially multiplied, allowing one unburdened professional to accomplish the work of ten while still owning every critical decision. While the alert queue will perpetually require attention, the fundamental nature of the work fundamentally changes. The timeline of a manual initial triage to active investigation compresses from a multi-hour ordeal into a matter of minutes. The data arrives clean, the investigation runs automatically, and the response mechanisms are prepared. The hours previously consumed by administrative waiting are directly reallocated to strategic decision-making.
When defensive systems are finally architected to operate at the speed of the modern threat landscape, the role of the human operator transforms. Analysts are no longer forced to act as passive passengers, grateful to be carried by fragmented tools. They are elevated to the role of pilots, operating with full situational awareness, retaining their judgment, and actively directing the defensive posture of the organization. This is the paradigm of the agentic SOC, and it is entirely predicated on the foundation of clean, structured data.
Contact us today to learn more about how SentinelOne is leading the way forward with Agentic SOC.
23andMe will pay $18 million to settle claims from 43 states over its 2023 data breach, which exposed genetic information tied to nearly 7 million people.
The post 23andMe Agrees to $18M Settlement With 43 States Over 2023 Data Breach appeared first on TechRepublic.
23andMe will pay $18 million to settle claims from 43 states over its 2023 data breach, which exposed genetic information tied to nearly 7 million people.
The post 23andMe Agrees to $18M Settlement With 43 States Over 2023 Data Breach appeared first on TechRepublic.
© Amelia Brust/Federal News Network
Bitcoin Magazine
Bitcoin Price Jumps Over $65,500 on Soft Inflation Data
The Bitcoin price jumped over $65,500 on Wednesday after US inflation data showed that producer prices fell in June.
Data from the Labor Department showed that the Producer Price Index posted its biggest decline in 14 months. The PPI, excluding food and energy, fell 0.3% in June, according to Bureau of Labor Statistics numbers.
Bitcoin’s price was recently trading at $64,943, a 2% 24-hour jump.
The Bitcoin price has typically surged when signs inflation is cooling emerge as investors then expect a bigger chance of lower interest rates. Crypto, stocks and other “risk-on” assets have in the past done well in a low-interest rate environment.
Still, the cooling inflation does not take into account the latest escalation in the US-Iran war: President Trump this week said the US would take control over the Strait of Hormuz.
On Wednesday, the US leader vowed to intensify the bombing until Tehran stops attacking ships in the Strait of Hormuz and agrees to open the waterway.
“We’re going to hit [Iran] very hard the night after,” President Trump told Fox News on Tuesday. “And then next week it gets really bad for them because next week comes the power plants.”
“The only way you can negotiate with these people is through strength,” he added.
Bitcoin’s price has faced increased volatility since the US and Israel attacked Iran on February 28, with the leading cryptocurrency dropping hard on initial reports of war.
Since the start of the year, the leading cryptocurrency has shed nearly 30% of its value, and is now close to 50% below the $126,080 record it notched in October.
Downwards pressure has been added to the Bitcoin price as US investors fast cashed out of spot exchange-traded funds throughout the month of June as inflation uncertainties and a boom in artificial intelligence-related stocks has led speculators to put their cash elsewhere.
JUST IN: Bitcoin rips to $65,374
— Bitcoin Magazine (@BitcoinMagazine) July 15, 2026
Figures released on Tuesday from June’s Consumer Price Index also showed that inflation appeared to be easing in the US, also leading to a jump in the Bitcoin price.
Over a seven-day period, Bitcoin’s price has traded from $61,507 to as high as $65,501.
Traders are now keeping an eye on what new Federal Reserve Chair Kevin Warsh — who’s typically been an inflation hawk in the past — will do while leading the central bank.
The new Chair told congress this week that the Federal Reserve has “no tolerance for persistently elevated inflation,” and that policy makers at the bank share “a resolute commitment to restoring price stability.”
Kevin Warsh was sworn in as the new central bank chief in May. The former Federal Reserve governor has said he wanted to lower the cost of borrowing but markets initially priced him in as a hawk — someone who would raise interest rates to tackle inflation.
At the time of writing, the bitcoin price is near $65,000.
This post Bitcoin Price Jumps Over $65,500 on Soft Inflation Data first appeared on Bitcoin Magazine and is written by Mathew Di Salvo.
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Summary Bullets:
AI agents are no longer an idea. They are now being deployed by enterprises to improve internal workplace productivity and external customer experience. At Google Cloud Summit Sydney (held on June 25, 2026), more examples of agentic AI in operations were presented, moving from deterministic AI chatbots to more autonomous systems. Bunnings, a home improvement, gardening, and hardware products retailer in Australia, upgraded its Buddy AI chatbot that helped customers with product search to an AI agent that takes customers’ descriptions of their projects and fills the shopping carts with the products that they need. Bunnings indicated an uplift of conversion rates and basket sizes when customers engage with Buddy. Similarly, Woolworths supermarket has an agentic AI powered Olive assistant that is able to build shopping baskets from recipe photos and assist with proactive meal planning. These two examples demonstrate how AI agents trained with proprietary knowledge (e.g., Bunnings’s DIY catalog and Woolworths’ recipe catalog) can deliver greater customer outcomes.
Enterprises deploying AI will appreciate the importance of data. To benefit from AI, it is necessary for enterprises to tap into corporate data to impart knowledge to AI agents. Google Cloud has the advantage in this area since enterprises have been adopting its products such as BigQuery to manage their data more effectively. Moreover, the company has other associated products such as Google Maps, Google Search, and Google Workspace that customers can leverage to enhance their AI capabilities. Transurban, an Australian road operations company and toll road operator, works with Google Cloud to transform its interaction with customers. While customer relationships are mainly transactional, Transurban now leverages Google Cloud’s solutions such as Gemini Enterprise, BigQuery, and Google Maps to power its Linkt app with the “Linkt AI” assistant, which proactively suggests optimal travel routes and toll options, dynamically adjusts schedules for prevailing weather, delivers timely account balance notifications, and offers discounted hotel and attraction bookings for upcoming road trips.
Data is the most valuable asset for enterprises particularly in the age of AI. Many companies across jurisdictions are increasingly concerned about security and sovereignty. Google Cloud offers a set of options for enterprises to meet their data and AI sovereignty requirements. It addresses not just the issue of data residency but also operational sovereignty and software sovereignty. Firstly, Google Cloud Data Boundary helps customers to meet data residency requirements through a set of controls, e.g., regions where data is stored, compliance programs, and external customer or partner managed encryption keys. This option allows enterprises to enjoy the benefits of hosting data in the public cloud for operational flexibility and high availability. Google Cloud is also offering support services with personnel meeting specific geographical locations as well as monitoring capabilities with real-time alerts when organization policy changes violate the defined compliance posture.
For customers that have a more stringent requirement on operational sovereignty, Google Cloud Dedicated addresses the need by enabling solutions to be operated by an independent local partner. The solution is hosted in a standalone, local instance of Google Cloud. The local partner maintains exclusive control over security-critical systems, identity management, authentication, etc. as well as controls over communication between Google and the Google Cloud Dedicated environment. For example, S3NS (a joint venture between Thales and Google Cloud that is headquartered in Paris, France) offers PREMI3NS services built on Google Cloud Dedicated for customers in Europe, now generally available in France. S3NS has achieved SecNumCloud 3.2 qualification from the French National Agency for the Security of Information Systems (ANSSI). Google Cloud Dedicated is also available in Germany (in preview).
For clients with the most stringent sovereignty requirements, Google Distributed Cloud (GDC) air-gapped allows complete isolation, without connectivity to an external network. The solution gives customers the flexibility to use general purpose compute and GPUs, and leverage open-source software. Google Cloud has also made its Gemini available in this air-gapped option, giving customers generative AI capabilities including automation, content generation, discovery and summarization. The GDC air-gapped solution is now deployed by many government agencies including those in Australia and Singapore within the Asia-Pacific region.
Besides sovereignty, Google Cloud has been bolstering its capability to offer stronger cyber defense. This includes the acquisition of Mandiant to add threat intelligence and incident response capabilities as well as Wiz for multi-cloud security defense. At the Google Cloud Summit, the company together with Wiz demonstrated how agentic AI can help to improve protection at scale and speed. Wiz is offering three AI agents with distinct roles: the Red Agent helps to uncover vulnerabilities and validate exploitable risks across web applications and APIs; the Blue Agent is the threat investigator that gathers evidence across cloud telemetry, runtime signals, and identity context to assess the severity of a threat and allow threats to be resolved more proactively; and the Green Agent is the investigation and remediation engine, identifying the root cause of a risk and the safest and most effective resolution. Wiz is known to provide security for cloud-native applications across major cloud environments including AWS, Microsoft Azure, Google Cloud, and Oracle. Following the completed acquisition on March 11, 2026, Wiz joins Google Cloud but operates independently to maintain its brand and key value proposition.
Google Cloud offers the full AI stack including applications and agents, AI models, data platforms, and infrastructure. It has also demonstrated strong momentum through broad customer references. However, the ability to drive AI adoption ultimately lies with its partner ecosystem and its willingness to support third-party products (including AI models) and help customers operate within a multi-cloud environment. Consulting partners such as Accenture and Mantel Group were featured at the Google Cloud event, and these partners play a crucial role in helping enterprises develop their business strategy around AI and implement solutions addressing data, security, governance, and other technology challenges.
The post Google Cloud Summit Sydney: Putting Agentic AI into Action appeared first on IT Connection.
Microsoft’s Workplace Check-in feature brings office-location sharing into Teams, giving APAC employers new technical, privacy and workforce-policy decisions to address.
The post Microsoft Teams Workplace Check-In Is Live: What APAC Employers Need to Know appeared first on TechRepublic.
Topics include growth hacking, game design, blockchain, AI, digital marketing, cybersecurity, copywriting, and big data.
The post Enhance Your Expertise Anytime with Unlimited Online Courses — Now $19.97 appeared first on TechRepublic.
Microsoft’s Workplace Check-in feature brings office-location sharing into Teams, giving APAC employers new technical, privacy and workforce-policy decisions to address.
The post Microsoft Teams Workplace Check-In Is Live: What APAC Employers Need to Know appeared first on TechRepublic.
Topics include growth hacking, game design, blockchain, AI, digital marketing, cybersecurity, copywriting, and big data.
The post Enhance Your Expertise Anytime with Unlimited Online Courses — Now $19.97 appeared first on TechRepublic.
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