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Online Privacy
Age Verification Apple Cyber Security Online Privacy

Apple Rolls Out Global Age-Verification System to Protect Kids Online

 

Apple has rolled out a new global age-verification system across its platforms, aimed at keeping kids safer online while helping developers comply with tightening child safety laws worldwide. The move targets both app downloads and in‑app experiences, with a particular focus on blocking underage access to adult‑rated content without sacrificing user privacy.

Under the new rules, users in countries such as Brazil, Australia and Singapore will be blocked from downloading apps rated 18+ unless Apple can confirm they are adults. Similar protections are being extended to parts of the United States, where states like Utah and Louisiana are introducing strict online age‑assurance laws, pushing platforms to verify whether users are children, teens or adults before allowing access to certain apps or features.This marks one of Apple’s strongest steps yet to align its App Store with regional regulations on children’s digital safety.

At the heart of the initiative is Apple’s privacy‑focused Declared Age Range API, which lets apps learn a user’s age category instead of their exact birthdate. Developers can use this signal to tailor content, enable or disable features, or trigger parental consent flows for younger users, while never seeing sensitive identity details. Apple says this design is meant to minimize data collection and reduce the risk of intrusive ID checks or third‑party age‑verification databases.

For parents, the age‑verification push builds on Apple’s existing child account system and content restrictions.Parents can already set up child profiles, choose age ranges and apply web content filters, and now those settings can flow through to third‑party apps via the new tools.This means a game, social app or streaming service can automatically recognize that a user is a child or teen and adjust what they can see or do without asking for new personal information.

For developers, Apple is introducing an expanded toolkit that includes the updated Declared Age Range API, new age‑rating properties in StoreKit, and improved server notifications to track compliance. These tools will be essential in regions where apps must prove they are screening out underage users from adult content or obtaining parental consent for significant changes. As more governments pass online safety laws, Apple’s global age‑verification framework is likely to become a key part of how the App Store balances regulatory demands with user privacy.
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Minor Safety
customer data privacy Cyber Security Data Privacy data security Digital Personal Data Protection internet safety for kids Minor Safety

Age Verification Laws for Social Media Raise Privacy Concerns and Enforcement Challenges

 

Across nations, governments push tighter rules limiting young users’ access to social media. Because of worries over endless scrolling, disturbing material online, or growing emotional struggles in teens, officials demand change. Minimum entry ages - often 13 or 16 - are now common in draft laws shaping platform duties. While debates continue, one thing holds: unrestricted teenage access faces mounting resistance. 

Still, putting such policies into practice stirs up both technological hurdles and concerns about personal privacy. To make sure people are old enough, services need proof - yet proving age typically means gathering private details. Meanwhile, current regulations push firms to keep data collection minimal. That tension forms what specialists call an “age-verification trap,” where tighter control over access can weaken safeguards meant to protect individual information. 

While many rules about age limits demand that services make "reasonable efforts" to block young users, clear guidance on checking someone's actual age is almost never included. One way firms handle this gap: they lean heavily on just two methods when deciding what to do. Starting off, identity checks require people to show their age using official ID or online identity tools. 

Although more reliable, keeping such data creates worries over privacy breaches. Handling vast collections of private details increases exposure to cyber threats. Security weakens when too much sensitive material gathers in one place. Age guesses shape the next method. By watching how someone uses a device, or analyzing video selfies with face-scanning tech, systems try to judge their years without asking for ID cards. 

Still, since these outcomes depend on likelihoods instead of confirmed proof, doubt remains part of the process. Some big tech firms now run these kinds of tools. While Meta applies face-based age checks on Instagram in select regions - asking certain users to send brief video clips if they seem underage - TikTok examines openly shared videos to guess how old someone might be. 

Elsewhere, Google and its platform YouTube lean on activity patterns; yet when doubt remains, they can ask for official identification or payment details. These steps aim at confirming ages without relying solely on stated information. Mistakes happen within these systems. Though meant to protect, they occasionally misidentify adults as children - leading to sudden account access issues. 

At times, underage individuals slip through gaps, using borrowed IDs or setting up more than one profile. Restrictions fail when shared credentials enter the picture. A single appeal can expose personal details when systems retain proof materials past their immediate need. Stored face scans, ID photos, or validation logs may linger just to satisfy legal checks. These files attract digital intrusions simply by existing. Every extra day they remain increases the chance of breach. 

Where identity infrastructure is weak, the difficulty grows. Biometrics might step in when official systems fall short. Oversight tends to be sparse, even as outside verifiers take on bigger roles. Still, shielding kids on the web without losing grip on private information is far from simple. When authorities roll out tighter rules for confirming age, the tools built to follow these laws could change how identities and personal details move through digital spaces.
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generative AI cybercrime
AI-powered hacking AWS security report Cyber Security FortiGate firewall attack Fortinet firewall breach generative AI cybercrime

AI-Powered Cybercrime Hits 600+ FortiGate Firewalls Across 55 Countries, AWS Warns

 

Cybercriminals using readily available generative AI tools managed to breach more than 600 internet-facing FortiGate firewalls across 55 countries within a little over a month, according to a recent incident analysis released by Amazon Web Services (AWS).

The operation, active between mid-January and mid-February, did not rely on sophisticated zero-day vulnerabilities. Instead, attackers automated large-scale attempts to access exposed systems by rapidly testing weak or reused credentials—essentially the digital equivalent of trying every unlocked door, but at high speed with the assistance of AI.

AWS investigators believe the operation was carried out by a financially motivated Russian-speaking group. The attackers scanned for publicly accessible FortiGate management interfaces, attempted to log in using commonly reused passwords, and once successful, extracted configuration files that provided detailed insight into the victims’ network environments.

According to AWS’s security team, the threat actors leveraged multiple commercially available AI tools to produce attack playbooks, scripts, and operational documentation. This allowed a relatively small or less technically advanced group to conduct a campaign that would typically require greater manpower and development effort. Analysts also discovered traces of AI-generated code and planning materials on compromised systems, indicating that AI tools were used extensively throughout the operation rather than just for occasional scripting tasks.

"The volume and variety of custom tooling would typically indicate a well-resourced development team," said CJ Moses, CISO at Amazon. "Instead, a single actor or very small group generated this entire toolkit through AI-assisted development."

After gaining access to the firewalls, the attackers retrieved configuration data containing administrator and VPN credentials, network architecture information, and firewall policies. Armed with these details, they attempted deeper intrusions by targeting directory services such as Active Directory, harvesting credentials, and exploring options for lateral movement across compromised networks. Backup infrastructure, including servers running Veeam, was also targeted during the intrusions.

AWS researchers noted that although the tools used in the campaign were functional, they appeared somewhat crude. The scripts showed basic parsing methods and repetitive comments often associated with machine-generated drafts. Despite their imperfections, the tools proved effective enough for large-scale automated attacks. When systems proved difficult to compromise, the attackers often abandoned them and shifted focus to easier targets, suggesting that their strategy prioritized volume over precision.

The affected organizations were spread across several regions, including Europe, Asia, Africa, and Latin America. The activity did not appear to focus on a single sector or country, indicating opportunistic targeting. However, investigators observed clusters of incidents suggesting that some breaches may have provided access to managed service providers or shared infrastructure, potentially increasing the scale of downstream exposure.

AWS emphasized that many of the compromises could have been avoided with standard cybersecurity practices. Preventing management interfaces from being publicly accessible, implementing multi-factor authentication, and avoiding password reuse would have significantly reduced the attackers’ chances of success.

The report comes shortly after Google cautioned that cybercriminal groups are increasingly integrating generative AI technologies—including tools such as Gemini AI—into their operations. These technologies are being used for tasks such as reconnaissance, target profiling, phishing campaign creation, and malware development


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malicious PDF files
critical vulnerabilities Cyber Attackers Cyber Hijack Cyber Security Foxit eSign Malicious PDF Attachment malicious PDF files

Researchers Find Critical Zero-Day Vulnerabilities in Foxit and Apryse PDF Platforms

 

PDF files are often seen as simple digital documents, but recent research shows they have evolved into complex software environments that can expose corporate systems to cyber risks. Modern PDF tools now function more like application platforms than basic viewers, potentially giving attackers pathways into private networks. 

A study by Novee Security examined two widely used platforms, Foxit and Apryse. Released on February 18, 2026, the report identified 13 categories of vulnerabilities and 16 potential attack paths that could allow systems to be compromised. 

Researchers say these issues are more than minor bugs. Some zero-day flaws could allow attackers to run commands on backend servers or take over user accounts without needing to compromise a browser or operating system. To find the vulnerabilities, analysts first identified common patterns that signal security weaknesses. These patterns were then used to train an AI system that scanned large volumes of code much faster than manual review alone. 

By combining human insight with automated analysis, the system detected several high-impact issues that conventional scanning tools might miss. One major flaw appeared in Foxit’s digital signature server, which verifies electronically signed documents. Some of the most serious findings involve one-click exploits where simply opening a document or loading a link can trigger malicious activity. Vulnerabilities CVE-2025-70402 and CVE-2025-70400 affect Apryse WebViewer by allowing the software to trust remote configuration files without proper validation, enabling attackers to run malicious scripts. 

Another flaw, CVE-2025-70401, showed that malicious code could be hidden in the “Author” field of a PDF comment and executed when a user interacts with it. Researchers also identified CVE-2025-66500, which affects Foxit browser plugins. In this case, manipulated messages could trick the plugin into running harmful scripts within the application. Testing further showed that certain weaknesses could allow attackers to send a simple request that triggers command execution on a server, granting unauthorized access to parts of the system. 

These vulnerabilities highlight how small interactions or overlooked behaviors can lead to significant security risks. Experts say the core problem lies in how modern PDF platforms are built. Many now rely on web technologies such as iframes and server-side processing, yet organizations still treat PDF files as harmless static documents. This mismatch can create “trust boundary” failures where software accepts external data without sufficient validation. 

Both vendors were notified before the research was published, and the vulnerabilities were assigned official CVE identifiers to support patching efforts. The findings highlight how document-processing systems—often overlooked in security planning—can become complex attack surfaces if not properly secured.
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ECB
AI Risks Banking Oversight Credit Risk Cyber Security ECB

ECB Tightens Oversight of Banks’ Growing AI Sector Risks

 

The European Central Bank is intensifying its oversight of how eurozone lenders finance the fast‑growing artificial intelligence ecosystem, reflecting concern that the boom in data‑centre and AI‑related infrastructure could hide pockets of credit and concentration risk.

In recent weeks, the ECB has sent targeted requests to a select group of major European banks, asking for granular data on their loans and other exposures to AI‑linked activities such as data‑centre construction, vendor financing and large project‑finance structures. Supervisors want to map where credit is clustering around a small set of hyperscalers, cloud providers and specialized hardware suppliers, amid global estimates of trillions of dollars in planned AI‑related capital spending. Officials stress this is a diagnostic exercise rather than an immediate step toward higher capital charges, but it marks a shift from general discussion to hands‑on information gathering.

The push comes as European banks race to harness AI inside their own operations, from credit scoring and fraud detection to automating back‑office tasks and enhancing customer service. Supervisors acknowledge that these technologies promise sizeable efficiency gains and new revenue opportunities, yet warn that many institutions still lack mature governance for AI models, including robust data‑quality controls, explainability, and clear accountability for automated decisions. The ECB has repeatedly argued that AI adoption must be matched by stronger risk‑management frameworks and continuous human oversight over model life cycles.

Regulators are also increasingly uneasy about systemic dependencies created by the dominance of a handful of mostly non‑EU AI and cloud providers. Heavy reliance on these external platforms raises concerns about operational resilience, data protection, and geopolitical risk that could spill over into financial stability if disruptions occur. At the same time, the ECB’s broader financial‑stability assessments have highlighted stretched valuations in some AI‑linked equities, warning that a sharp correction could transmit stress into bank balance sheets through both direct exposures and wider market channels. 

For now, supervisors frame their AI‑sector review as part of a wider effort to “encourage innovation while managing risks,” aligning prudential expectations with Europe’s new AI Act and digital‑operational‑resilience rules. Banks are being nudged to tighten contract terms, strengthen model‑validation teams and improve documentation before scaling AI‑driven business lines. The message from Frankfurt is that AI remains welcome as a driver of competitiveness in European finance—but only if lenders can demonstrate they understand, measure and contain the new concentrations of credit, market and operational risk that accompany the technology’s rapid rise.
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Threat Intelligence
AI Summit AI Threat Cyber Security DeepMind Threat Intelligence

DeepMind Chief Sounds Alarm on AI's Dual Threats

 

Google DeepMind CEO Sir Demis Hassabis has issued a stark warning on the escalating threats posed by artificial intelligence, urging immediate action from governments and tech firms. In an exclusive BBC interview at the AI Impact Summit in Delhi, he emphasized that more research into AI risks "needs to be done urgently," rather than waiting years. Hassabis highlighted the industry's push for "smart regulation" targeting genuine dangers from increasingly autonomous systems.

The AI pioneer identified two primary threats: malicious exploitation by bad actors and the potential loss of human control over super-capable AI systems. He stressed that current fragmented efforts in safety research are insufficient, with massive investments in AI development far outpacing those in oversight and evaluation. As AI models grow more powerful, Hassabis warned of a "narrow window" to implement robust safeguards before existing institutions are overwhelmed.

Speaking at the summit, which concluded recently in India's capital, Hassabis called for scaled-up funding and talent in AI safety science. He compared the challenge to nuclear safety protocols, arguing that advanced AI now demands societal-level treatment with rigorous testing before widespread deployment. The event brought together global leaders to discuss AI's societal impacts amid rapid advancements.

Hassabis advocated for international cooperation, noting AI's borderless nature means it affects everyone worldwide. He praised forums like those in the UK, Paris, and Seoul for uniting technologists and policymakers, while pushing for minimum global standards on AI deployment.However, tensions exist, as the US delegation at the Delhi summit rejected global AI governance outright.

This comes as AI capabilities surge, with systems learning physical realities and approaching artificial general intelligence (AGI) in 5-10 years. Hassabis acknowledged natural constraints like hardware shortages may slow progress, providing time for safeguards, but stressed proactive measures are essential. Industry leaders must balance innovation with risk mitigation to harness AI's potential safely.

Safety recommendations 

To counter AI threats, organizations should prioritize independent safety evaluations and red-teaming exercises before deploying models. Governments must fund public AI safety research grants and enforce "smart regulations" focused on real risks like misuse and loss of control. Individuals can stay vigilant by verifying AI-generated content, using tools like watermark detectors, limiting data shared with AI systems, and supporting ethical AI policies through advocacy.
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regulatory policies
AI Risk Management Cyber Security MSP cybersecurity organizations regulatory policies

AI-Driven Risk Management Is Becoming a Key Growth Strategy for MSPs

 



Expanding cybersecurity services as a Managed Service Provider (MSP) or Managed Security Service Provider (MSSP) requires more than strong technical capabilities. Providers also need a sustainable business approach that can deliver clear and measurable value to clients while supporting growth at scale.

One approach gaining attention across the cybersecurity industry is risk-based security management. When implemented effectively, this model can strengthen trust with customers, create opportunities to offer additional services, and establish stable recurring revenue streams. However, maintaining such a strategy consistently requires structured workflows and the right supporting technologies.

To help providers adopt this approach, a new resource titled “The MSP Growth Guide: How MSPs Use AI-Powered Risk Management to Scale Their Cybersecurity Business” outlines how organizations can transition toward scalable cybersecurity services centered on risk management. The guide provides insights into the operational difficulties many MSPs encounter, offers recommendations from industry experts, and explains how AI-driven risk management platforms can help build a more scalable and profitable service model.


Why Risk-Focused Security Enables Service Expansion

Many MSPs already deliver essential cybersecurity capabilities such as endpoint protection, regulatory compliance assistance, and other defensive tools. While these services remain critical, they are often delivered as separate engagements rather than as part of a unified strategy. As a result, the long-term strategic value of these services may remain limited, and opportunities to generate consistent recurring revenue may be reduced.

Adopting a risk-centered cybersecurity framework can shift this dynamic. Instead of addressing isolated technical issues, providers evaluate the complete threat environment facing a client organization. Security risks are then prioritized according to their potential impact on business operations.

This broader perspective allows MSPs to move away from reactive fixes and instead deliver continuous, proactive security management.

Organizations that implement this risk-first model can gain several advantages:

• Security teams can detect and address threats before they escalate into damaging incidents.

• Defensive measures can be continuously updated as the cyber threat landscape evolves.

• Critical assets, daily operations, and organizational reputation can be protected even when compliance regulations do not explicitly require certain safeguards.

Another major benefit is alignment with modern cybersecurity frameworks. Many current standards require companies to conduct formal and ongoing risk evaluations. By integrating risk management into their core service offerings, MSPs can position themselves to pursue higher-value contracts and offer additional services driven by regulatory compliance requirements.


Common Obstacles That Limit Risk Management Services

Although risk-focused security delivers substantial value, MSPs often encounter operational barriers that make these services difficult to scale or demonstrate clearly to clients.

Several recurring challenges affect service delivery and growth:

Manual assessment processes

Traditional risk evaluations often rely heavily on manual work. This approach can consume a vast majority of time, introduce inconsistencies, and make it difficult to expand services efficiently.

Lack of actionable remediation plans

Risk reports sometimes underline security weaknesses but fail to outline clear steps for resolving them. Without defined guidance, clients may struggle to understand how to address the issues that have been identified.

Complex regulatory alignment

Organizations frequently need to comply with multiple cybersecurity standards and regulatory frameworks. Managing these requirements manually can create inefficiencies and inconsistencies.

Limited business context in security reports

Many security assessments are written in highly technical language. As a result, business leaders and non-technical stakeholders may find it difficult to interpret the results or understand the real impact on their organization.

Shortage of specialized cybersecurity professionals

Skilled risk management experts remain in high demand across the industry, making it difficult for service providers to recruit and retain qualified personnel.

Third-party risk visibility gaps

Many cybersecurity platforms focus only on internal infrastructure and overlook risks introduced by external vendors and service providers.

These challenges can make it difficult for MSPs to transform risk management into a scalable and profitable cybersecurity offering.


How AI-Powered Platforms Help Address These Barriers

To overcome these operational difficulties, many providers are turning to artificial intelligence-driven risk management tools.

AI-based platforms can automate large portions of the risk management process. Tasks that previously required extensive manual effort, such as risk assessment, prioritization, and reporting, can be completed more quickly and consistently.

These systems are designed to streamline the entire risk management lifecycle while incorporating advanced security expertise into service delivery.


What Modern Risk Management Platforms Should Deliver

A well-designed AI-enabled risk management solution should do more than simply detect potential threats. It should also accelerate service delivery and support business growth for service providers.

Organizations adopting these platforms can expect several operational benefits:

• Faster onboarding and service deployment through automated and easy-to-use risk assessment tools

• More efficient compliance management supported by built-in mappings to cybersecurity frameworks and continuous monitoring capabilities

• Clearer reporting that presents cybersecurity risks in language business leaders can understand

• Demonstrable return on investment by reducing manual workloads and enabling more efficient service delivery

• Additional revenue opportunities by identifying new cybersecurity services clients may require based on their risk profile


Key Capabilities to Evaluate When Selecting a Platform

Selecting the right technology platform is critical for service providers that want to scale cybersecurity operations effectively.

Several capabilities are considered essential in modern risk management tools:

Automated risk assessment systems

Automation allows providers to generate assessment results within days rather than months, while minimizing human error and ensuring consistent outcomes.


Dynamic risk registers and visual risk mapping

Visualization tools such as heatmaps help security teams quickly identify which risks pose the greatest threat and should be addressed first.


Action-oriented remediation planning

Effective platforms convert risk findings into structured and prioritized tasks aligned with both compliance obligations and business objectives.


Customizable risk tolerance frameworks

Organizations can adapt risk scoring models to match each client’s specific operational priorities and appetite for risk.

The MSP Growth Guide provides additional details on the features providers should consider when evaluating potential solutions.


Building Long-Term Strategic Value with AI-Driven Risk Management

For MSPs and MSSPs seeking to expand their cybersecurity practices, AI-powered risk management offers a way to deliver consistent value while improving operational efficiency.

By automating risk assessments, prioritizing security issues based on business impact, and standardizing reporting processes, these platforms enable providers to deliver reliable cybersecurity services to a growing client base.

The guide “The MSP Growth Guide: How MSPs Use AI-Powered Risk Management to Scale Their Cybersecurity Business” explains how service providers can integrate AI-driven risk management into their offerings to support long-term growth.

Organizations interested in strengthening customer relationships, expanding cybersecurity services, and building a competitive advantage may benefit from adopting risk-focused security strategies supported by AI-enabled platforms.


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Malware Attack
AI Risks APT36 APT36 Cyber Espionage APT36 hackers Cloud Security cyber espionage Cyber Espionage Campaign Cyber Security Google Sheets API abuse Malware Attack

APT36 Uses AI-Generated “Vibeware” Malware and Google Sheets to Target Indian Government Networks

 

Researchers at Bitdefender have uncovered a new cyber campaign linked to the Pakistan-aligned threat group APT36, also known as Transparent Tribe. Unlike earlier operations that relied on carefully developed tools, this campaign focuses on mass-produced AI-generated malware. Instead of sophisticated code, the attackers are pushing large volumes of disposable malicious programs, suggesting a shift from precision attacks to broad, high-volume activity powered by artificial intelligence. Bitdefender describes the malware as “vibeware,” referring to cheap, short-lived tools generated rapidly with AI assistance. 

The strategy prioritizes quantity over accuracy, with attackers constantly releasing new variants to increase the chances that at least some will bypass security systems. Rather than targeting specific weaknesses, the campaign overwhelms defenses through continuous waves of new samples. To help evade detection, many of the programs are written in lesser-known programming languages such as Nim, Zig, and Crystal. Because most security tools are optimized to analyze malware written in more common languages, these alternatives can make detection more difficult. 

Despite the rapid development pace, researchers found that several tools were poorly built. In one case, a browser data-stealing script lacked the server address needed to send stolen information, leaving the malware effectively useless. Bitdefender’s analysis also revealed signs of deliberate misdirection. Some malicious files contained the common Indian name “Kumar” embedded within file paths, which researchers believe may have been placed to mislead investigators toward a domestic source. In addition, a Discord server named “Jinwoo’s Server,” referencing a popular anime character, was used as part of the infrastructure, likely to blend malicious activity into normal online environments. 

Although some tools appear sloppy, others demonstrate more advanced capabilities. One component known as LuminousCookies attempts to bypass App-Bound Encryption, the protection used by Google Chrome and Microsoft Edge to secure stored credentials. Instead of breaking the encryption externally, the malware injects itself into the browser’s memory and impersonates legitimate processes to access protected data. The campaign often begins with social engineering. Victims receive what appears to be a job application or resume in PDF format. Opening the document prompts them to click a download button, which silently installs malware on the system. 

Another tactic involves modifying desktop shortcuts for Chrome or Edge. When the browser is launched through the altered shortcut, malicious code runs in the background while normal browsing continues. To hide command-and-control activity, the attackers rely on trusted cloud platforms. Instructions for infected machines are stored in Google Sheets, while stolen data is transmitted through services such as Slack and Discord. Because these services are widely used in workplaces, the malicious traffic often blends in with routine network activity. 

Once inside a network, attackers deploy monitoring tools including BackupSpy. The program scans internal drives and USB storage for specific file types such as Word documents, spreadsheets, PDFs, images, and web files. It also creates a manifest listing every file that has been collected and exfiltrated. Bitdefender describes the overall strategy as a “Distributed Denial of Detection.” Instead of relying on a single advanced tool, the attackers release large numbers of AI-generated malware samples, many of which are flawed. However, the constant stream of variants increases the likelihood that some will evade security defenses. 

The campaign highlights how artificial intelligence may enable cyber groups to produce malware at scale. For defenders, the challenge is no longer limited to identifying sophisticated attacks, but also managing an ongoing flood of low-quality yet constantly evolving threats.
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