Bondu’s AI plush toy exposed a web console that let anyone with a Gmail account read about 50,000 private chats between children and their cuddly toys.

Bondu’s toy is marketed as:

“A soft, cuddly toy powered by AI that can chat, teach, and play with your child.”

What it doesn’t say is that anyone with a Gmail account could read the transcripts from virtually every child who used a Bondu toy. Without any actual hacking, simply by logging in with an arbitrary Google account, two researchers found themselves looking at children’s private conversations.

What Bondu has to say about safety does not mention security or privacy:

“Bondu’s safety and behavior systems were built over 18 months of beta testing with thousands of families. Thanks to rigorous review processes and continuous monitoring, we did not receive a single report of unsafe or inappropriate behavior from Bondu throughout the entire beta period.”

Bondu’s emphasis on successful beta testing is understandable. Remember the AI teddy bear marketed by FoloToy that quickly veered from friendly chat into sexual topics and unsafe household advice?

The researchers were stunned to find the company’s public-facing web console allowed anyone to log in with their Google account. The chat logs between children and their plushies revealed names, birth dates, family details, and intimate conversations. The only conversations not available were those manually deleted by parents or company staff.

Potentially, these chat logs could been a burglar’s or kidnapper’s dream, offering insight into household routines and upcoming events.

Bondu took the console offline within minutes of disclosure, then relaunched it with authentication. The CEO said fixes were completed within hours, they saw “no evidence” of other access, and they brought in a security firm and added monitoring.

In the past, we’ve pointed out that AI-powered stuffed animals may not be a good alternative for screen time. Critics warn that when a toy uses personalized, human‑like dialogue, it risks replacing aspects of the caregiver–child relationship. One Curio founder even described their plushie as a stimulating sidekick so parents, “don’t feel like you have to be sitting them in front of a TV.”

So, whether it’s a foul-mouth, a blabbermouth, or just a feeble replacement for real friends, we don’t encourage using Artificial Intelligence in children’s toys—unless we ever make it to a point where they can be used safely, privately, securely, and even then, sparingly.

How to stay safe

AI-powered toys are coming, like it or not. But being the first or the cutest doesn’t mean they’re safe. The lesson history keeps teaching us is this: oversight, privacy, and a healthy dose of skepticism are the best defenses parents have.

• Turn off what you can. If the toy has a removable AI component, consider disabling it when you’re not able to supervise directly.
• Read the privacy policy. Yes, I know, all of it. Look for what will be recorded, stored, and potentially shared. Pay particular attention to sensitive data, like voice recordings, video recordings (if the toy has a camera), and location data.
• Limit connectivity. Avoid toys that require constant Wi-Fi or cloud interaction if possible.
• Monitor conversations. Regularly check in with your kids about what the toy says and supervise play where practical.
• Keep personal info private. Teach kids to never share their names, addresses, or family details, even with their plush friend.
• Trust your instincts. If a toy seems to cross boundaries or interfere with natural play, don’t be afraid to step in or simply say no.

---

We don’t just report on privacy—we offer you the option to use it.

Privacy risks should never spread beyond a headline. Keep your online privacy yours by using Malwarebytes Privacy VPN.

Introducing the 2026 Intezer AI SOC Report for CISOs

For years, security leaders have lived with an uncomfortable truth. It has been to date, simply impossible to investigate every alert. As alert volumes exploded and teams failed to scale, SOCs, whether in-house or outsourced, normalized “acceptable risk” with the deprioritization of low-severity and informational alerts.

Our latest research shows that this approach is no longer defensible.

Intezer has just released the 2026 AI SOC Report for CISOs, based on the forensic analysis of more than 25 million security alerts across live enterprise environments. The findings reveal a critical disconnect between how security teams prioritize alerts and where real threats actually originate, and the cost of that gap is far higher than most organizations realize .

Why “acceptable risk” is no longer acceptable 

Across endpoint, cloud, identity, network, and phishing telemetry, Intezer found that nearly 1% of confirmed incidents originated from alerts initially labeled as low-severity or informational. On endpoints, that figure climbed to nearly 2%.

At enterprise scale, that percentage is not noise.

For a typical organization generating roughly 450,000 alerts per year, this translates to ~50 real threats annually, about one per week, never investigated by a SOC or MDR team. These are not theoretical risks. They are real compromises hiding in plain sight, dismissed not because they were benign, but because teams lacked the capacity to look.

What the data revealed across the attack surface

Because Intezer AI SOC investigates 100% of alerts using forensic-grade analysis, the report exposes how attackers actually operate once you remove triage bias from the equation.

Endpoint security is more fragile than reported

More than half of endpoint alerts were not automatically mitigated by endpoint protection tools. Of those, nearly 9% were confirmed malicious. Even more concerning, 1.6% of endpoints undergoing live forensic scans were still actively compromised despite being reported as “mitigated” by EDR tools.

See the full endpoint threat data → Download the 2026 AI SOC Report

Low-severity does not mean low-risk

Within endpoint alerts alone, 1.9% of low-severity and informational alerts were real incidents, the exact alerts most SOCs never review.

Attackers favor stealth over noise

Cloud telemetry was dominated by defense evasion and persistence techniques, reflecting a shift toward long-term access, token abuse, and misuse of legitimate services rather than overt exploitation.

Phishing has moved into trusted platforms and browsers

Fewer than 6% of malicious phishing emails contained attachments. Most relied on links, language, and abuse of legitimate services such as cloud file sharing, code sandboxes, CAPTCHA mechanisms, where traditional controls have limited visibility.

Cloud misconfigurations persist as silent risk multipliers

Most cloud posture findings stemmed from legacy or default configurations, especially in Amazon S3, including missing encryption, weak access controls, and lack of logging—issues often classified as “low severity,” yet repeatedly exploited once attackers gain a foothold.

To read the full report and all the findings, download the CISOs guide to AI SOC 2026 here. 

Why traditional SOCs fail: capacity, fragmentation and judging alerts by their severity

Modern SOC failures are rarely the result of a single broken tool or negligent team. They are the outcome of structural tradeoffs that every traditional SOC—internal or MDR—has been forced to make.

Capacity is the first constraint.
Human analysts do not scale linearly with alert volume. As telemetry expands across endpoint, cloud, identity, network, and SaaS, SOCs hit a hard ceiling. The only way to cope is aggressive triage: close most alerts automatically, investigate only what looks “important,” and hope severity labels align with reality. The 2026 AI SOC Report shows that this assumption is false at scale.

Tool fragmentation compounds the problem.
Most SOC stacks are collections of siloed detections, EDR, SIEM, identity, cloud posture, email, each optimized for a narrow signal. Severity is assigned locally, without cross-surface context or forensic validation. As a result, alerts are scored based on abstract rules, not evidence of compromise. When SOCs trust these labels blindly, they inherit the tools’ blind spots.

Process tradeoffs lock risk in place.
Once triage rules are defined, they become institutionalized. Low-severity alerts are ignored by design. MDR providers codify this into SLAs. Internal SOCs bake it into runbooks. Crucially, there is no closed-loop feedback: missed threats do not automatically improve detections, because they were never investigated in the first place.

The outcome is not an occasional failure. It is systematic, repeatable risk, embedded directly into how SOCs operate.

Real-world examples of missed threats hiding in plain sight

The data in the 2026 AI SOC Report makes clear that missed threats are not exotic edge cases. They are ordinary attacks progressing quietly through environments because no one looked.

Endpoints marked “mitigated” but still compromised
In over 1.6% of live forensic endpoint scans, Intezer found active malicious code running in memory even though the EDR had already reported the threat as resolved. These cases included stealers, RATs, and post-exploitation frameworks, often originating from low-severity alerts that never triggered deeper inspection. Without memory-level forensics, these compromises would have remained invisible.

Phishing hosted on trusted platforms
Attackers increasingly host phishing pages on legitimate developer platforms like Vercel and CodePen, or abuse trusted cloud services such as OneDrive and PayPal. The parent domains appear reputable, so alerts are downgraded or ignored. Yet behind them are live credential-harvesting pages that bypass email gateways and browser-based defenses alike.

Cloud misconfigurations as delayed breach accelerators
Many cloud posture findings such as unencrypted S3 buckets, missing access logs and permissive cross-account policies rarely trigger action. But once an attacker gains any foothold, these long-standing misconfigurations dramatically accelerate lateral movement, persistence, and data exposure.

In every case, the failure was not detection. The signal existed. The failure was investigation.

How attackers deliberately exploit SOC blind spots

Attackers understand SOC economics better than most defenders.

They know which alerts generate fatigue.
They know which detections are noisy.
They know which categories are deprioritized by default.

As a result, modern attackers design their campaigns to blend into the backlog, not trigger alarms.

Stealth over speed
Cloud intrusions favor defense evasion, persistence, and token abuse over loud exploitation. These behaviors generate alerts, but rarely high-severity ones. The report shows cloud telemetry dominated by exactly these tactics, indicating attackers are optimizing for long-term access rather than immediate impact.

Living off trusted infrastructure
Phishing campaigns increasingly abuse legitimate brands, file-sharing services, CAPTCHA frameworks, and developer platforms. These environments inherit trust by default, allowing attackers to operate under severity thresholds that SOCs routinely ignore.

Multi-stage loaders and memory-only execution
On endpoints, attackers rely on layered loaders, in-memory payloads, and obfuscation techniques that evade static detections. Initial alerts may look benign or incomplete. Without forensic follow-through, SOCs miss the actual compromise entirely.

Attackers are not evading detection systems alone, rather they are exploiting SOC decision-making models.

What this means for your SOC operations

For CISOs and SOC leaders, the implication is stark:
Risk is no longer defined by what you detect, but by what you choose not to investigate.

If your SOC:

• Ignores low-severity alerts by default
  
• Relies on severity labels without forensic validation
  
• Limits investigations based on human capacity
  
• Operates without a feedback loop between outcomes and detections
  

Then missed threats are not anomalies, they are guaranteed.

The organizations that will reduce risk in 2026 are not adding more dashboards or rewriting triage rules. They are adopting operating models where investigation is no longer a scarce resource.

This is why AI-driven, forensic-grade SOC platforms fundamentally change the equation. When every alert is investigated:

• Severity becomes evidence-based, not assumed
  
• Detection quality improves through real-world validation
  
• Attackers lose the ability to hide in “acceptable risk”
  
• SOC teams regain control without scaling headcount
  

This is the shift behind the Intezer AI SOC model and why the concept of acceptable risk must be redefined for the modern threat landscape.

This all changes when you can investigate everything

The data in the 2026 AI SOC Report points to a different reality, one where AI-driven forensic analysis removes investigation capacity as a constraint.

When every alert is investigated:

• “Low severity” stops being a proxy for “safe”
  
• Detection quality improves through real-world validation
  
• Missed threats drop from dozens per year to near zero
  
• Escalations fall below 2%, without sacrificing coverage
  
• Risk tolerance is defined by evidence, not exhaustion
  

This is the operating model behind Intezer AI SOC, powered by ForensicAI™ and it is why the definition of acceptable risk must be reset.

Download the report and join the discussion

The 2026 AI SOC Report for CISOs is grounded in:

• 25 million alerts analyzed
  
• 10 million monitored endpoints and identities
  
• 82,000 forensic endpoint investigations, including live memory scans
  
• Telemetry from 7 million IP addresses, 3 million domains and URLs, and over 550,000 phishing emails
  

All data was aggregated and anonymized across Intezer’s global enterprise customer base.

👉 Download the full report to explore the findings in detail, and
👉 Join Intezer’s research team on Wednesday, February 4th at 12 p.m. ET for a live webinar breaking down what this data means for SOC leaders and CISOs.

Because in 2026, the biggest risk is no longer what you detect, it’s what you choose not to investigate.

The post Alert fatigue is costing you: Why your SOC misses 1% of real threats appeared first on Intezer.

Russia's current isolation from the Olympics may lead to increased cyberthreats targeting the 2026 Winter Games. We discuss the potential threat picture.

The post Understanding the Russian Cyberthreat to the 2026 Winter Olympics appeared first on Unit 42.

A significant number of modern incidents begin with account compromise. Since initial access brokers have become a full-fledged criminal industry, it’s become much easier for attackers to organize attacks on companies’ infrastructure by simply purchasing sets of employee passwords and logins. The widespread practice of using various remote access methods has made their task even easier. At the same time, the initial stages of such attacks often look like completely legitimate employee actions, and remain undetected by traditional security mechanisms for a long time.

Relying solely on account protection measures and password policies isn’t an option. There’s always a chance that attackers will get hold of employees’ credentials using various phishing attacks, infostealer malware, or simply through the carelessness of employees who reuse the same password for work and personal accounts and don’t pay much attention to leaks on third-party services.

As a result, to detect attacks on a company’s infrastructure, you need tools that can detect not only individual threat signatures, but also behavioral analysis systems that can detect deviations from normal user and system processes.

Using AI in SIEM to detect account compromise

As we mentioned in our previous post, to detect attacks involving account compromise, we equipped our Kaspersky Unified Monitoring and Analysis Platform SIEM system with a set of UEBA rules designed to detect anomalies in authentication processes, network activity, and the execution of processes on Windows-based workstations and servers. In the latest update, we continued to develop the system in the same direction, adding the use of AI approaches.

The system creates a model of normal user behavior during authentication, and tracks deviations from usual scenarios: atypical login times, unusual event chains, and anomalous access attempts. This approach allows SIEM to detect both authentication attempts with stolen credentials, and the use of already compromised accounts, including complex scenarios that may have gone unnoticed in the past.

Instead of searching for individual indicators, the system analyzes deviations from normal patterns. This allows for earlier detection of complex attacks while reducing the number of false positives, and significantly reduces the operational load on SOC teams.

Previously, when using UEBA rules to detect anomalies, it was necessary to create several rules that performed preliminary work and generated additional lists in which intermediate data was stored. Now, in the new version of SIEM with a new correlator, it’s possible to detect account hijacking using a single specialized rule.

Other updates in the Kaspersky Unified Monitoring and Analysis Platform

The more complex the infrastructure and the greater the volume of events, the more critical the requirements for platform performance, access management flexibility, and ease of daily operation become. A modern SIEM system must not only accurately detect threats, but also remain “resilient” without the need to constantly upgrade equipment and rebuild processes. Therefore, in version 4.2, we’ve taken another step toward making the platform more practical and adaptable. The updates affect the architecture, detection mechanisms, and user experience.

Addition of flexible roles and granular access control

One of the key innovations in the new version of SIEM is a flexible role model. Now customers can create their own roles for different system users, duplicate existing ones, and customize a set of access rights for the tasks of specific specialists. This allows for a more precise differentiation of responsibilities among SOC analysts, administrators, and managers, reduces the risk of excessive privileges, and better reflects the company’s internal processes in the SIEM settings.

New correlator and, as a result, increased platform stability

In release 4.2, we introduced a beta version of a new correlation engine (2.0). It processes events faster, and requires fewer hardware resources. For customers, this means:

• stable operation under high loads;
• the ability to process large amounts of data without the need for urgent infrastructure expansion;
• more predictable performance.

TTP coverage according to the MITRE ATT&CK matrix

We’re also systematically continuing to expand our coverage of the MITRE ATT&CK matrix of techniques, tactics, and procedures: today, Kaspersky SIEM covers more than 60% of the entire matrix. Detection rules are regularly updated and accompanied by response recommendations. This helps customers understand which attack scenarios are already under control, and plan their defense development based on a generally accepted industry model.

Other improvements

Version 4.2 also introduces the ability to back up and restore events, as well as export data to secure archives with integrity control, which is especially important for investigations, audits, and regulatory compliance. Background search queries have been implemented for the convenience of analysts. Now, complex and resource-intensive searches can be run in the background without affecting priority tasks. This speeds up the analysis of large data sets.

 

We continue to regularly update Kaspersky SIEM, expanding detection capabilities, improving architecture, and adding AI functionality so that the platform best meets the real-world conditions of information security teams, and helps not only to respond to incidents, but also to build a sustainable protection model for the future. Follow the updates to our SIEM system, the Kaspersky Unified Monitoring and Analysis Platform, on the official product page.

Meta plans to test exclusive features that will be incorporated in paid versions of Facebook, Instagram, and WhatsApp. It confirmed these plans to TechCrunch.

But these plans are not to be confused with the ad-free subscription options that Meta introduced for Facebook and Instagram in the EU, the European Economic Area, and Switzerland in late 2023 and framed as a way to comply with General Data Protection Regulation (GDPR) and Digital Markets Act requirements.

From November 2023, users in those regions could either keep using the services for free with personalized ads or pay a monthly fee for an ad‑free experience. European rules require Meta to get users’ consent in order to show them targeted ads, so this was an obvious attempt to recoup advertising revenue when users declined to give that consent.

This year, users in the UK were given the same choice: use Meta’s products for free or subscribe to use them without ads. But only grudgingly, judging by the tone in the offer… “As part of laws in your region, you have a choice.”

That ad-free option, however, is not what Meta is talking about now.

The newly announced plans are not about ads, and they are also separate from Meta Verified, which starts at around $15 a month and focuses on creators and businesses, offering a verification badge, better support, and anti‑impersonation protection.

Instead, these new subscriptions are likely to focus on additional features—more control over how users share and connect, and possibly tools such as expanded AI capabilities, unlimited audience lists, seeing who you follow that doesn’t follow you back, or viewing stories without the poster knowing it was you.

These examples are unconfirmed. All we know for sure is that Meta plans to test new paid features to see which ones users are willing to pay for and how much they can charge.

Meta has said these features will focus on productivity, creativity, and expanded AI.

My opinion

Unfortunately, this feels like another refusal to listen.

Most of us aren’t asking for more AI in our feeds. We’re asking for a basic sense of control: control over who sees us, what’s tracked about us, and how our data is used to feed an algorithm designed to keep us scrolling.

Users shouldn’t have to choose between being mined for behavioral data or paying a monthly fee just to be left alone. The message baked into “pay or be profiled” is that privacy is now a luxury good, not a default right. But while regulators keep saying the model is unlawful, the experience on the ground still nudges people toward the path of least resistance: accept the tracking and move on.

Even then, this level of choice is only available to users in Europe.

Why not offer the same option to users in the US? Or will it take stronger US privacy regulation to make that happen?

---

We don’t just report on threats – we help protect your social media

Cybersecurity risks should never spread beyond a headline. Protect your social media accounts by using Malwarebytes Identity Theft Protection.