Rules and policies that control how APIs can connect with third parties and internally are a critical foundation of modern apps. At a high level, connectivity policies dictate the terms of engagement between APIs and their consumers. At a more granular level, Platform Ops teams need to ensure that APIs can meet service-level agreements and respond to requests quickly across a distributed environment. At the same time, connectivity overlaps with security: API connectivity rules are essential to ensure that data isn’t lost or leaked, business logic is not abused and brute-force account takeover attacks cannot target APIs. This is the domain of the API gateway. Unfortunately, most API gateways are designed primarily for north-south traffic. East-west traffic policies and rules are equally critical because in modern cloud native applications, there’s actually far more east-west traffic among internal APIs and microservices than north-south traffic to and from external customers.
While some fraud in the metaverse can be expected to resemble the scams and tricks of our ‘real-world’ society, other types of fraud must be quickly understood if they are to be mitigated by metaverse makers. When Facebook’s Metaverse first launched, investors rushed to pour billions of dollars into buying acres of land. The so-called ‘virtual real estate’ sparked a land boom which saw $501 million in sales in 2021. This year, that figure is expected to grow to $1 billion. Selling land in the metaverse works like this: pieces of code are partitioned to create individual ‘plots’ within certain metaverse platforms. These are then made available to purchase as NFTs on the blockchain. While we might have laughed when one buyer paid hundreds of thousands of dollars to be Snoop Dogg’s neighbour in the metaverse, this is no laughing matter when it comes to security. Money spent in the metaverse is real, and fraudsters are out to steal it. One of the dangers of the metaverse is that, while the virtual land and property aren’t real, their monetary value is. On purchase, they become real assets linked to your account. Therefore, fraud doesn’t look like it used to.
Massive, unstructured IoT data workloads — typically stored at the edge or on-premise — require infrastructure that not only handles big data inflows, but directs traffic to ensure that data gets where it needs to be without disruption or downtime. This is no easy feat when it comes to data sets in the petabyte and exabyte range, but this is the essential challenge: prioritizing the real-time activation of data at scale. By building a foundation that optimizes the capture, migration, and usage of IoT data, these companies can unlock new business models and revenue streams that fundamentally alter their effects on the world around us. ... As legacy companies start to embrace their IoT data, cloud service providers should take notice. Cloud adoption, long understood to be a priority among businesses looking to better understand their consumers, will become increasingly central to the transformation of traditional companies. The cloud and the services delivered around it will serve as a highway for manufacturers or utilities to move, activate, and monetize exabytes of data that are critical to businesses across industries.
There is a plethora of tools being used to secure assets, including desktops, laptops, servers, virtual machines, smartphones, and cloud instances. But despite this, companies can struggle to identify which of their assets are missing the relevant endpoint protection platform/endpoint detection and response (EPP/EDR) agent defined by their security policy. They may have the correct agent but fail to understand why its functionality has been disabled, or they are using out-of-date versions of the agent. The importance of understanding which assets are missing the proper security tool coverage and which are missing the tools’ functionality cannot be underestimated. If a company invests in security and then suffers a malware attack because it has failed to deploy the endpoint agent, it is a waste of valuable resources. Agent health and cyber hygiene depends on knowing which assets are not protected, and this can be challenging. The admin console of an EPP/EDR can provide information about which assets have had the agent installed, but it does not necessarily prove that the agent is performing as it should.
PRIME develops a robust prediction model that isn’t easily tricked by adversarial cases to overcome this restriction. To architect simulators, this model is simply optimized using any standard optimizer. More crucially, unlike previous methods, PRIME can learn what not to construct by utilizing existing datasets of infeasible accelerators. This is accomplished by supplementing the learned model’s supervised training with extra loss terms that particularly punish the learned model’s value on infeasible accelerator designs and adversarial cases during training. This method is similar to adversarial training. One of the main advantages of a data-driven approach is that it enables learning highly expressive and generalist optimization objective models that generalize across target applications. Furthermore, these models have the potential to be effective for new applications for which a designer has never attempted to optimize accelerators. The trained model was altered to be conditioned on a context vector that identifies a certain neural net application desire to accelerate to train PRIME to generalize to unseen applications.
In a ZT environment, the network not only doesn’t trust a node new to it, but it also doesn’t trust nodes that are already communicating across it. When a node is first seen by a ZT network, the network will require that the node go through some form of authentication and authorization check. Does it have a valid certificate to prove its identity? Is it allowed to be connected where it is based on that identity? Is it running valid software versions, defensive tools, etc.? It must clear that hurdle before being allowed to communicate across the network. In addition, the ZT network does not assume that a trust relationship is permanent or context free: Once it is on the network, a node must be authenticated and authorized for every network operation it attempts. After all, it may have been compromised between one operation and the next, or it may have begun acting aberrantly and had its authorizations stripped in the preceding moments, or the user on that machine may have been fired.
Many industry experts said they were worried about the possibility of increased surveillance from governments, police and the technology companies that run the online platforms. Other concerns were around the protection of financial data from hackers if end-to-end encryption was undermined. There were concerns that wider sharing of “secret keys”, or centralised management of encryption processes, would significantly increase the risk of compromising the confidentiality they are meant to preserve. BCS’s Mitchell said: “It’s odd that so much focus has been on a magical backdoor when other investigative tools aren’t being talked about. Alternatives should be looked at before limiting the basic security that underpins everyone’s privacy and global free speech.” Government and intelligence officials are advocating, among other ways of monitoring encrypted material, technology known as client-side scanning (CSS) that is capable of analysing text messages on phone handsets and computers before they are sent by the user.
A majority of popular NFT projects so far have been focused on profile pictures and art projects, where early adopters have shown a willingness to jump through hoops and bear the burden of high transaction fees on the Ethereum Network. There’s growing enthusiasm for NFTs that serve more utilitarian purposes, like unlocking bonus content for subscription services or as a unique token to allow access to experiences and events. With the release of Hypernet.Mint, Hypernet Labs is taking the same approach toward simplifying the user experience that it applied to Hypernet.ID. Hypernet.Mint offers lower-cost deployment by leveraging Layer 2 blockchains like Polygon and Avalanche that don’t have the same high fee structure as the Ethereum mainnet. The company also helps dApps create a minting strategy that aligns with business goals, supporting either mass minting or minting that is based on user onboarding flows that may acquire additional users over time. “We’re working on a lot of onboarding flow for new types of users, which comes back to ease of use for users,” Ravlich said.
While AI can be a somewhat nebulous concept, decision intelligence is more concrete. That’s because DI is outcome-focused: a decision intelligence solution must deliver a tangible return on investment before it can be classified as DI. A model for better stock management that gathers dust on a data scientist’s computer isn’t DI. A fully productionised model that enables a warehouse team to navigate the pick face efficiently and decisively, saving time and capital expense — that’s decision intelligence. Since DI is outcome focused, it requires models to be built with an objective in mind and so addresses many of the pain points for businesses that are currently struggling to quantify value from their AI strategy. By working backwards from an objective, businesses can build needed solutions and unlock value from AI quicker. ... Global companies, including Pepsico, KFC and ASOS have already emerged as early adopters of DI, using it to increase profitability and sustainability, reduce capital requirements, and optimise business operations.
Software quality is not always an indicator of secure software. A measure of secure software is the number of vulnerabilities uncovered during testing and after production deployment. Software vulnerabilities are a sub-category of software bugs that threat actors often exploit to gain unauthorized access or perform unauthorized actions on a computer system. Authorized users also exploit software vulnerabilities, sometimes with malicious intent, targeting one or more vulnerabilities known to exist on an unpatched system. These users can also unintentionally exploit software vulnerabilities by inputting data that is not validated correctly, subsequently compromising its integrity and the reliability of those functions that use the data. Vulnerability exploits target one or more of the three security pillars; Confidentiality, Integrity, or Availability, commonly referred to as the CIA Triad. Confidentiality entails protecting data from unauthorized disclosure; Integrity entails protecting data from unauthorized modification and facilitates data authenticy.
Quote for the day:
"To be a good leader, you don't have to know what you're doing; you just have to act like you know what you're doing." -- Jordan Carl Curtis