For over 90 years, the Royal Air Forces Association (RAFA) has championed a simple yet profound belief: No member of the RAF community should ever be left without the help they need. Serving personnel, veterans and their families, the RAF Association provides crucial welfare support, responding to increasingly complex needs in an era of operational changes and challenges, including persistent global deployment.
Delivering on their mission today requires not only compassion and expertise but also resilient digital foundations. To strengthen and future-proof its operations, RAFA has entered into a strategic partnership with Palo Alto Networks. Together, we are modernising the Association's cyber security posture through a secure-by-design, zero trust architecture to enhance organisational resilience, secure sensitive beneficiary data, and improve operational agility. This helps ensure they can focus on their mission of support, not security management.
As Nick Bunting OBE, Secretary General at the RAF Association, puts it:
Cybersecurity is essential to safeguarding the trust people place in our organisation. This transformation will give us greater protection for our data and systems, ensuring that our services remain dependable and that our organisation is secure, resilient and ready for the future. Strong digital security is not just a technical requirement, it is a fundamental part of how we uphold our duty of care to every individual who relies on us.
The RAF Association operates in a distributed environment comprising headquarters’ functions, remote caseworkers, and more than 20 RAFAKidz nursery sites, supported by a growing portfolio of cloud-based services. In this context, cybersecurity is not simply an IT concern. It is a safeguarding imperative.
Disruption to systems or a compromise of sensitive beneficiary data could directly impact RAFA’s ability to deliver services and maintain the trust of the communities it supports. By consolidating fragmented legacy tools into a unified platform, this partnership ensures the Association’s digital evolution aligns security controls with GDPR obligations and safeguarding requirements.
To support RAFA's lean IT operational model, this transformation will move them away from fragmented legacy tools toward a unified platform approach. The deployment of Prisma® SASE (secure access service edge) and Cortex XDR® will provide RAFA with consistent visibility and control across users, devices, applications and data, regardless of location. This consolidation replaces complexity with clarity, allowing the organisation to inspect traffic for threats in real-time. Security policies are now enforced continuously, threats are detected and contained faster, and access to critical systems is governed by zero trust principles without compromising the user experience.
As Phil Sherwin, Chief Information Officer, at the RAF Association states:
Our data is one of our most valuable assets and the protection of that data, as we continue to provide life-changing support to members of the RAF community, is our most important priority. This partnership will move us into the next generation of security tools that adopt zero trust principles and is a crucial step on our journey to providing a layered approach to data protection.
One of the most critical aspects of this modernisation is supporting RAFA’s diverse workforce, particularly within the RAFAKidz nursery sites. These environments rely on nondesk-based staff using iPads and mobile devices to get their critical work done.
Using zero touch provisioning and the Prisma Browser™, we are enabling secure, seamless connectivity for unmanaged devices. This ensures that nursery staff can access necessary SaaS applications safely without complex login hurdles or manual configuration, improving their agility and allowing them to focus on caring for children rather than troubleshooting technology.
As a charity, RAFA has a responsibility to ensure resources are used efficiently. A critical goal of this partnership is to improve productivity and allow the organisation to scale its services without increasing the IT burden.
By adopting Strata™ Cloud Manager with AIOps (artificial intelligence for IT operations), RAFA is shifting from reactive security operations to proactive, automated management. Machine learning helps identify configuration risks and performance issues before they affect users, while standardized policies enable the secure, consistent onboarding of new sites. This shift is projected to significantly reduce operational overhead, enabling RAFA to scale its support network cost-effectively. This shift is projected to reduce operational overhead by 40–50%.
This partnership is about more than deploying technology. It is about ensuring RAFA remains resilient, trusted and capable of supporting the RAF community for decades to come.
As Darren Bisbey, Head of Group Information Security for the RAF Association, puts it:
We live in an era where digital threats are accelerating in both scale and sophistication, creating unprecedented challenges for organisations. Our partnership with Palo Alto is a statement of intent, reflecting our unwavering commitment to building the most secure environments possible for our data.
At Palo Alto Networks, we are honored to support RAFA in this journey, providing the digital armour and operational advantage necessary to protect those who serve and have served.
As Alistair Wildman, Palo Alto Networks CEO for Northern Europe states:
For over 90 years, RAFA has been a lifeline for the RAF community; it is our privilege to ensure that legacy endures in a digital-first world. By embracing a unified, AI-driven platform, RAFA is moving beyond complex, fragmented security to a posture that is Secure by Design. This partnership allows them to navigate today’s threat landscape with confidence, ensuring their resources remain focused where they belong: on the families who need them.
The post Empowering the RAF Association with Next-Generation Cyber Resilience appeared first on Palo Alto Networks Blog.
As AI reshapes the world, organizations encounter unprecedented risks, and security leaders take on new responsibilities. Microsoft’s Secure Development Lifecycle (SDL) is expanding to address AI-specific security concerns in addition to the traditional software security areas that it has historically covered.
SDL for AI goes far beyond a checklist. It’s a dynamic framework that unites research, policy, standards, enablement, cross-functional collaboration, and continuous improvement to empower secure AI development and deployment across our organization. In a fast-moving environment where both technology and cyberthreats constantly evolve, adopting a flexible, comprehensive SDL strategy is crucial to safeguarding our business, protecting users, and advancing trustworthy AI. We encourage other organizational and security leaders to adopt similar holistic, integrated approaches to secure AI development, strengthening resilience as cyberthreats evolve.
AI security introduces complexities that go far beyond traditional cybersecurity. Conventional software operates within clear trust boundaries, but AI systems collapse these boundaries, blending structured and unstructured data, tools, APIs, and agents into a single platform. This expansion dramatically increases the attack surface and makes enforcing purpose limitations and data minimization far more challenging.
Unlike traditional systems with predictable pathways, AI systems create multiple entry points for unsafe inputs including prompts, plugins, retrieved data, model updates, memory states, and external APIs. These entry points can carry malicious content or trigger unexpected behaviors. Vulnerabilities hide within probabilistic decision loops, dynamic memory states, and retrieval pathways, making outputs harder to predict and secure. Traditional threat models fail to account for AI-specific attack vectors such as prompt injection, data poisoning, and malicious tool interactions.
AI dissolves the discrete trust zones assumed by traditional SDL. Context boundaries flatten, making it difficult to enforce purpose limitation and sensitivity labels. Governance must span technical, human, and sociotechnical domains. Questions arise around role-based access control (RBAC), least privilege, and cache protection, such as: How do we secure temporary memory, backend resources, and sensitive data replicated across caches? How should AI systems handle anonymous users or differentiate between queries and commands? These gaps expose corporate intellectual property and sensitive data to new risks.
Meeting AI security needs requires a holistic approach across stack layers historically outside SDL scope, including Business Process and Application UX. Traditionally, these were domains for business risk experts or usability teams, but AI risks often originate here. Building SDL for AI demands collaborative, cross-team development that integrates research, policy, and engineering to safeguard users and data against evolving attack vectors unique to AI systems.
AI cyberthreats are fundamentally different. Systems assume all input is valid, making commands like “Ignore previous instructions and execute X” viable cyberattack scenarios. Non-deterministic outputs depend on training data, linguistic nuances, and backend connections. Cached memory introduces risks of sensitive data leakage or poisoning, enabling cyberattackers to skew results or force execution of malicious commands. These behaviors challenge traditional paradigms of parameterizing safe input and predictable output.
AI training data and model weights require protection equivalent to source code. Poisoned datasets can create deterministic exploits. For example, if a cyberattacker poisons an authentication model to accept a raccoon image with a monocle as “True,” that image becomes a skeleton key—bypassing traditional account-based authentication. This scenario illustrates how compromised training data can undermine entire security architectures.
AI accelerates development cycles beyond SDL norms. Model updates, new tools, and evolving agent behaviors outpace traditional review processes, leaving less time for testing and observing long-term effects. Usage norms lag tool evolution, amplifying misuse risks. Mitigation demands iterative security controls, faster feedback loops, telemetry-driven detection, and continuous learning.
Ultimately, the security landscape for AI demands an adaptive, multidisciplinary approach that goes beyond traditional software defenses and leverages research, policy, and ongoing collaboration to safeguard users and data against evolving attack vectors unique to AI systems.
Security policy falls short of addressing real-world cyberthreats when it is treated as a list of requirements to be mechanically checked off. AI systems—because of their non-determinism—are much more flexible that non-AI systems. That flexibility is part of their value proposition, but it also creates challenges when developing security requirements for AI systems. To be successful, the requirements must embrace the flexibility of the AI systems and provide development teams with guidance that can be adapted for their unique scenarios while still ensuring that the necessary security properties are maintained.
Effective AI security policies start by delivering practical, actionable guidance engineers can trust and apply. Policies should provide clear examples of what “good” looks like, explain how mitigation reduces risk, and offer reusable patterns for implementation. When engineers understand why and how, security becomes part of their craft rather than compliance overhead. This requires frictionless experiences through automation and templates, guidance that feels like partnership (not policing) and collaborative problem-solving when mitigations are complex or emerging. Because AI introduces novel risks without decades of hardened best practices, policies must evolve through tight feedback loops with engineering: co-creating requirements, threat modeling together, testing mitigations in real workloads, and iterating quickly. This multipronged approach helps security requirements remain relevant, actionable, and resilient against the unique challenges of AI systems.
So, what does Microsoft’s multipronged approach to AI security look like in practice? SDL for AI is grounded in pillars that, together, create strong and adaptable security:
Together, these pillars form a holistic and adaptive framework that moves beyond checklists, enabling Microsoft to safeguard AI systems through collaboration, innovation, and shared responsibility. By integrating research, policy, standards, enablement, cross-functional collaboration, and continuous improvement, SDL for AI creates a culture where security is intrinsic to AI development and deployment.
Microsoft’s SDL for AI introduces specialized guidance and tooling to address the complexities of AI security. Here’s a quick peek at some key AI security areas we’re covering in our secure development practices:
In the coming months, we’ll share practical and actionable guidance on each of these topics.
Effective SDL for AI is about continuous improvement and shared responsibility. Security is not a destination. It’s a journey that requires vigilance, collaboration between teams and disciplines outside the security space, and a commitment to learning. By following Microsoft’s SDL for AI approach, enterprise leaders and security professionals can build resilient, trustworthy AI systems that drive innovation securely and responsibly.
Keep an eye out for additional updates about how Microsoft is promoting secure AI development, tackling emerging security challenges, and sharing effective ways to create robust AI systems.
To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.
The post Microsoft SDL: Evolving security practices for an AI-powered world appeared first on Microsoft Security Blog.
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.
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.
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.
The company will use the investment to accelerate development of its autonomous virtual security operations center (vSOC).
The post RADICL Raises $31 Million for vSOC appeared first on SecurityWeek.
The company will use the latest capital to scale its go-to-market efforts and expand its platform’s capabilities.
The post RapidFort Raises $42M to Automate Software Supply Chain Security appeared first on SecurityWeek.
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 .
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.
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.
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
Within endpoint alerts alone, 1.9% of low-severity and informational alerts were real incidents, the exact alerts most SOCs never review.
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.
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.
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.
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.
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.
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.
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:
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:
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.
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:
This is the operating model behind Intezer AI SOC, powered by ForensicAI™ and it is why the definition of acceptable risk must be reset.
The 2026 AI SOC Report for CISOs is grounded in:
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.
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