Kubernetes configurations are primarily defined using YAML files, which are human-readable data serialization standards. However, the simplicity of YAML is deceptive, as small errors can lead to significant Security vulnerabilities. One common mistake is improper indentation or formatting, which can cause the configuration to be applied incorrectly or not at all. ... The ransomware attack on the Toronto Public Library revealed the critical importance of network microsegmentation in Kubernetes environments. By limiting network access to necessary resources only, microsegmentation is pivotal in preventing the spread of attacks and safeguarding sensitive data. ... eBPF is the basis for creating a universal “security blanket” across Kubernetes clusters, and is applicable on premises, in the public cloud and at the edge. Its integration at the kernel level allows for immediate detection of monitoring gaps and seamless application of security measures to new and changing clusters. eBPF can automatically apply predefined security policies and monitoring protocols to any new cluster within the environment.

The best strategy, says Sam Lucero, chief quantum analyst at Omdia, would be to combine multiple approaches to get the error rates down even further. ... The bigger question is which type of qubit is going to become the standard – if any. “Different types of qubits might be better for different types of computations,” he says. This is where early testing can come in. High-performance computing centers can already buy quantum computers, and anyone with a cloud account can access one online. Using quantum computers via a cloud connection is much cheaper and quicker. Plus, it gives enterprises more flexibility, says Lucero. “You can sign on and say, ‘I want to use IonQ’s trapped ions. And, for my next project, I want to use Regetti, and for this other project, I want to use another computer.’” But stand-alone quantum computers aren’t necessarily the best path forward for the long term, he adds. “If you’ve got a high-performance computing capability, it will have GPUs for one type of computing, quantum processing units for another type of computing, CPUs for another type of computing – and it’s going to be transparent to the end user,” he says. “The system will automatically parcel it out to the appropriate type of processor.”

One of the biggest debates is how much security hybridization offers. Much depends on the details and the algorithm designers can take any number of approaches with different benefits. There are several models for hybridization and not all the details have been finalized. Encrypting the data first with one algorithm and then with a second combines the strength of both, essentially putting a digital safe inside a digital safe. Any attacker would need to break both algorithms. However, the combinations don’t always deliver in the same way. For example, hash functions are designed to make it hard to identify collisions, that is two different inputs that produce the same output: (x_1 and x_2, such that h(x_1)=h(x_2)). If the input of the first hash function is fed into a second different hash function (say g(h(x))), it may not get any harder to find a collision, at least if the weakness lies in the first function. If two inputs to the first hash function produce the same output, then that same output will be fed into the second hash function to generate a collision for the hybrid system: (g(h(x_1))= g(h(x_2)) if h(x_1)=h(x_2)). Digital signatures are also combined differently than encryption. One of the simplest approaches is to just calculate multiple signatures independently from each other. 

The MSSPs have a significant opportunity for growth, with an increasing number of partners showing interest in this domain. What’s notable is that our focus isn’t solely on partners delivering network security solutions but also extends to other offerings. For instance, our SIEM solutions now feature a consumption-based model, attracting more partners to explore the realm of MSSP partnerships. This trend has already gained momentum over the past year, indicating a promising trajectory for the future. As the market continues to expand, catering to a diverse range of customers across various sizes and sectors, the demand for managed security services will only intensify. Here, our integrator partners play a crucial role, positioned to capitalise on the growing requirements of clients. Moreover, selected MSSP partners have the opportunity to develop specialised services around Fortinet solutions, leveraging programs like FortiDirect, FortiEDR, FortiWeb, and FortiMail. Our offerings, such as the MSSP Monitor program and Flex VM program, provide flexible consumption models tailored to the evolving needs of MSP partners. 

One in four organizations say gen AI is critically important to gaining increased productivity and efficiency. Thirty percent say improving customer experience and personalization is their highest priority, and 26% say it’s the technology’s potential to improve decision-making that matters most. ... “The generative AI phenomenon has captured the attention of the market—and the world—with both positive and negative connotations,” said Howard Dresner, founder, and chief research officer at Dresner Advisory. “While generative AI adoption remains nascent in the near term, a strong majority of respondents indicate intentions to adopt it early or in the future.” ... Nearly half of organizations consider data privacy to be a critical concern in their decision to adopt gen AI. Legal and regulatory compliance, the potential for unintended consequences, and ethics and bias concerns are also significant. Less than half of respondents—46% and 43%, respectively—consider costs and organizational policy important to generative AI adoption. Weaponized LLMs and attacks on chatbots fuel fears over data privacy. More organizations are fighting back and using gen AI to protect against chatbot leaks.

Is it the data set, i.e. volume of data? The number of parameters used? The transformer model? The encoding, decoding, and fine-tuning? The processing time? The answer is of course a combination of all of the above. It is often said that GenAI Large Language Models (LLMs) and Natural Language Processing (NLP) require large amounts of training data. However, measured in terms of traditional data storage, this is not actually the case. ... It is thought that ChatGPT-3 was trained on 45 Terabytes of Commoncrawl plaintext, filtered down to 570GB of text data. It is hosted on AWS for free as its contribution to Open Source AI data. But storage volumes, the billions of web pages or data tokens that are scraped from the Web, Wikipedia, and elsewhere then encoded, decoded, and fine-tuned to train ChatGPT and other models, should have no major impact on a data center. Similarly, the terabytes or petabytes of data needed to train a text-to-speech, text to image or text-to-video model should put no extraordinary strain on the power and cooling systems in a data center built for hosting IT equipment storing and processing hundreds or thousands of petabytes of data.

Just as software-oriented architecture (SOA) evolved application architecture from monolithic applications into microservices patterns, IaC has been the slow-burn movement that is challenging what the base building blocks should be for how we think of cloud infrastructure. IaC really got on the map in the 2010s, when Puppet, Chef, and Ansible introduced IaC methods for the configuration of virtual machines. Chef was well-loved for allowing developers to use programming languages like Ruby and for the reuse and sharing that came with being able to use the conventions of a familiar language. During the next decade, the IaC movement entered a new era as the public cloud provider platforms matured, and Kubernetes became the de facto cloud operating model. HashiCorp’s Terraform became the IaC poster child, introducing new abstractions for the configuration of cloud resources and bringing a domain-specific language (DSL) called HashiCorp Configuration Language (HCL) designed to spare developers from lower-level cloud infrastructure plumbing.

Cloud computing has emerged as a robust and secure platform for data storage, offering unparalleled protection against extreme conditions and disasters. Today’s cloud-based providers offer robust security and disaster recovery capabilities, ensuring the safety and integrity of critical data assets. ... This includes empowering doctors and nurses to access patient records securely on their own devices and facilitating remote consultations through virtual desktop infrastructure (VDI). This instant access has transformed the way healthcare professionals interact with patient data, allowing doctors to review charts on tablets during rounds and nurses to retrieve medication histories from any workstation. By storing data on secure servers rather than end-client devices, cloud-based solutions guarantee the protection of critical medical records in the event of theft or compromise of an end device. ... This approach not only ensures data security but also meets the stringent requirements of healthcare institutions while allowing for scalable systems connected to the hospital’s network.

One of the key challenges we face is managing time effectively in an environment where the line between ‘on’ and ‘off’ hours is increasingly fuzzy. AI-powered tools and analytics can generate insights and tasks round-the-clock, leading to an ‘always-on’ work culture. This can encroach upon personal time, making it challenging to disconnect and potentially causing stress and burnout. Maintaining mental health in this context is paramount. It is incumbent upon companies to ensure that the implementation of AI and analytics tools does not exacerbate workplace stress. Instead, these tools should be leveraged to promote a healthier work-life balance by automating routine tasks, predicting workload peaks, and enabling flexible working arrangements. Achieving personal fulfillment in the age of AI also means embracing lifelong learning. As the nature of work evolves, so too must our skillsets. Upskilling and reskilling become not just a means to professional advancement but also an opportunity for personal growth and satisfaction. Analytics can play a role here in identifying skill gaps and learning opportunities that align with individual career paths and interests.

Hackers love exploiting APIs for many reasons, but mostly because they let them bypass security controls and access sensitive company and customer data easily, as well as certain functionalities. A recent incident involving a publicly exposed API of social media platform Spoutible could have ended in attackers stealing users’ 2FA secrets, encrypted password reset tokens, and more. This type of incident can result in a loss of customer and business partners’ trust, consequently leading to financial loss and a drop in brand value. Poor Api Security practices can also have regulatory and legal consequences, cause disruption to company operations and even result in intellectual property theft. ... A good API security strategy is essential for every organization that wants to keep its digital assets safe and protect sensitive customer data. OWASP constantly updates its list of the top 10 API security threats. While security practitioners mustn’t rely solely on this data, the list is still an essential tool when planning a security strategy that will hold up. Adhering to the NIST Cybersecurity Framework is also an essential step in planning a good API security strategy.