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Apple iPhone 16 Pro vs Samsung Galaxy S24
This time around we’re comparing the the smallest flagships from the two largest smartphone manufacturers in the world. This is the Apple iPhone 16 Pro vs Samsung Galaxy S24 comparison. Granted, the iPhone 16 Pro is not the base model in the iPhone 16 series, but it is the smallest flagship in the series aka the smallest ‘Pro’ iPhone 16 model. So, this comparison does make sense, as the Galaxy S24 is by far the smallest smartphone in the Galaxy S24 family.
With that being said, the iPhone 16 vs Galaxy S24 comparison is also on the way. The iPhone 16 Pro is notably more expensive than the Galaxy S24, so keep that in mind. We will first list the specifications of these two smartphones, and will then move to compare them across a number of different sections. We’ll compare the designs of the two phones, their displays, performance, battery, cameras, and audio output. Let’s get down to it.
Specs
Apple iPhone 16 Pro vs Samsung Galaxy S24, respectively
– Screen size:
6.3-inch LTPO Super Retina XDR OLED ( flat, 120Hz, HDR, 2,000 nits max brightness)
6.2-inch Dynamic AMOLED 2X (flat, 120Hz, 2,600 nits max brightness)
– Display resolution:
2622 x 1206
2340 x 1080
– SoC:
Apple A18 Pro (3nm)
Qualcomm Snapdragon 8 Gen 3/Samsung Exynos 2400
– RAM:
8GB
8GB (LPDDR5X)
– Storage:
128GB/256GB/512GB/1TB (NVMe)
128GB (UFS 3.1)/256GB/512GB (UFS 4.0)
– Rear cameras:
48MP (wide, f/1.8 aperture, 1/1.28-inch sensor, 1.22um pixel size, sensor-shift OIS), 48MP (ultrawide, f/2.2 aperture, 0.7um pixel size, PDAF), 12MP (periscope telephoto, f/2.8 aperture, 1/3.06-inch sensor, 1.12um pixel size, 3D sensor-shift OIS, 5x optical zoom)
50MP (wide, f/1.8 aperture, OIS, Dual Pixel PDAF), 12MP (ultrawide, 120-degree FoV, f/2.2 aperture, 1.4um pixel size), 10MP (telephoto, f/2.4 aperture, OIS, PDAF, 3x optical zoom)
– Front cameras:
12MP (f/1.9 aperture, PDAF, 1/3.6-inch sensor size, OIS)
12MP (wide, f/2.2 aperture, Dual Pixel PDAF, 22mm lens)
– Battery:
3,582mAh
4,000mAh
– Charging:
38W wired, 25W MagSafe wireless, 15W Qi2 wireless, 7.5W Qi wireless, 5W reverse wired
25W wired, 15W wireless, 4.5W reverse wireless (charger not included)
– Dimensions:
149.6 x 71.5 x 8.3 mm
147 x 70.6 x 7.6mm
– Weight:
199 grams
167/168 grams
– Connectivity:
5G, LTE, NFC, Wi-Fi, USB Type-C, Bluetooth 5.3
– Security:
Face ID (3D facial scanning)
Ultrasonic in-display fingerprint scanner
– OS:
iOS 18
Android 14 with One UI 6.1
– Price:
$999+
$799.99+
– Buy:
Apple iPhone 16 Pro
Samsung Galaxy S24 (Best Buy)
Apple iPhone 16 Pro vs Samsung Galaxy S24: Design
The iPhone 16 Pro is made out of titanium and glass. On the flip side, the Galaxy S24 utilizes aluminum and glass. Both smartphones have flat sides all around, which are curved towards the very edges. They both include flat front and back sides too, and have a similar curvature on the edges. Well, the iPhone 16 Pro is curved more in that area, but neither phone is close to having sharp edges.
Apple’s handset has a pill-shaped cutout at the top of the display, the so-called Dynamic Island. Samsung’s device has a small display camera hole up there. Both devices do have very thin bezels around the display, which are also uniform. On the right-hand side of the iPhone 16 Pro you’ll find a power/lock key and the Camera Control button. On the left, the volume up and down buttons are located, along with an Action Button. The Galaxy S24, on the other hand, has the power/lock key on the right, along with the volume up and down buttons, and that’s it.
Both smartphones have three cameras on the back, but those setups look considerably different. The iPhone 16 Pro has its recognizable camera island in the top-left corner. The Galaxy S24’s cameras protrude directly from the backplate and are vertically-aligned in the top-left corner. The iPhone 16 Pro does have a slightly bigger display, and it’s taller and wider than the Galaxy S24, while also being thicker and heavier. It’s over 30 grams heavier. Both smartphones offer an IP68 certification for water and dust resistance. They’re both quite slippery too, but very comfortable to hold.
Apple iPhone 16 Pro vs Samsung Galaxy S24: Display
The iPhone 16 Pro features a 6.3-inch 2622 x 1206 LTPO Super Retina XDR OLED display. That panel is flat, and it has a 120Hz refresh rate. HDR10 content is supported, as is Dolby Vision. The maximum brightness here is set at 2,000 nits. The screen-to-body ratio is at around 90%, while the display aspect ratio is 19.5:9. The Ceramic Shield glass is placed on top of this phone’s display.
The Samsung Galaxy S24, on the flip side, has a 6.2-inch 2340 x 1080 Dynamic LTPO AMOLED 2X display. This display has a 120Hz refresh rate and supports HDR10+ content. It also offers a 2,600 nits peak brightness. The screen-to-body ratio is at around 90%, while the display aspect ratio is 19.5:9. The Gorilla Glass Victus 2 from Corning is protecting this phone’s display.
Both of these panels are really good. They’re quite vivid and more than sharp enough. They also have very good viewing angles, and the touch response is very good. These displays do not have a high-frequency PWM dimming, though, so keep that in mind. The blacks are deep on both, and both have a high refresh rate. The Galaxy S24 can technically get brighter, but in practice, the difference is not that big at all. They’re both bright enough.
Apple iPhone 16 Pro vs Samsung Galaxy S24: Performance
The Apple A18 Pro is a 3nm processor which fuels the iPhone 16 Pro. That is Apple’s most powerful chip. The company also included 8GB of RAM here, along with NVMe flash storage. The Galaxy S24 is fueled by the Snapdragon 8 Gen 3 (4nm) or Exynos 2400 (4nm) chips, depending on the market. We used the Snapdragon 8 Gen 3 model. Samsung also included 8GB of LPDDR5X RAM inside the phone, along with UFS 3.1 or UFS 4.0 flash storage. UFS 3.1 flash storage is included in the 128GB storage option only.
Having said that, both smartphones do offer really good performance. In regular, day-to-day tasks, they both perform great. They’re snappy whatever you’re doing, and the high refresh rate helps keep things looking really nice while you’re scrolling around. Getting either phone to slow down is not that easy. They can jump between apps without a problem and are great for browsing, messaging, emailing, multimedia consumption, image editing, video processing, and so on.
The iPhone 16 Pro technically has more prowess on the gaming side of things. It has a more powerful chip and GPU, but the Galaxy S24 keeps up in terms of performance. No matter what game you throw at these two phones, they’ll do a great job. They will get warm after a while, but neither phone will get visibly affected by that, at all. Neither phone becomes to hot to hold either.
Apple iPhone 16 Pro vs Samsung Galaxy S24: Battery
The iPhone 16 Pro battery capacity has finally been revealed, the phone includes a 3,582mAh battery, so a 9.4% larger battery pack than its predecessor. The Galaxy S24 includes a 4,000mAh battery pack. Apple’s iPhones usually have smaller battery packs than their Android counterparts. In this case the difference is not that big, and the iPhone 16 Pro does offer better battery life in comparison… it’s not even close.
The Galaxy S24 can even struggle to get to the 6-hour screen-on-time mark, it tends to be closer to 5-5.5 hours. The iPhone 16 Pro can go above and beyond that. The iPhone 15 Pro offered really good battery life, and the iPhone 16 Pro flies above that. Getting to the 7-hour screen-on-time mark on this phone does seem doable, but it will depend on a number of factors, of course. Your mileage may vary.
When it comes to charging, the iPhone 16 Pro supports 38W wired, 25W MagSafe wireless, 15W Qi2 wireless, 7.5W Qi wireless, and 5W reverse wired charging. The Galaxy S24 supports 25W wired, 15W wireless, and 4.5W reverse wireless charging. Do note that neither of these two smartphones ships with a charger in the retail box. You’ll have to buy one separately if you don’t already own it.
Apple iPhone 16 Pro vs Samsung Galaxy S24: Cameras
You’ll find three cameras on the back of both of these phones. The iPhone 16 Pro has a 48-megapixel main camera (1/1.28-inch camera sensor), a 48-megapixel ultrawide unit, and a 12-megapixel periscope telephoto camera (5x optical zoom). The Galaxy S24 includes a 50-megapixel main camera (1/1.56-inch camera sensor), a 12-megapixel ultrawide unit (120-degree FoV), and a 10-megapixel telephoto unit (3x optical zoom).
Both of these phones do a good job in the camera department, but the iPhone 16 Pro pulls ahead. It has a more capable main camera, and that shows in the final product. Both phones tend to provide images with warmer tones, but the ones from the iPhone 16 Pro have a better balance overall. The Galaxy S24 can overdo it with sharpening and saturation at times, the photos also don’t look as well-rounded. The iPhone 16 Pro does tend to brighten up the darker portions of images in HDR situations a bit too much, which makes the images look flatter than it should. They both do a very good job in low light, but once again, the iPhone 16 Pro is better most of the time.
The iPhone 16 Pro has a telephoto camera that offers more versatility in comparison, and the shots from it mostly look a bit better. Its ultrawide camera also tends to provide more detail than Samsung’s, but both do a good job of keeping the color profile similar to what their main shooters provide.
Audio
Stereo speakers are included on both smartphones, and they both offer good performance. The sound output is well-balanced, and not too sharp or anything. They’re both loud enough and similar in that regard.
There is no audio jack on either one of these two smartphones, though. You’ll need to use their Type-C ports if you want to hook up your wired headphones. Alternatively, Bluetooth 5.3 is on offer for wireless connectivity.
Technology
Adobe Sued by US Government
In this episode of News of The Week, the US government has filed a lawsuit against Adobe, accusing the software giant of deceptive subscription practices that make it difficult for users to cancel their subscriptions.
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InfluxData targets performance, adds self-managed version
InfluxData on Wednesday unveiled new features for its InfluxDB 3.0 product suite aimed at speeding and simplifying time series data management at scale, including performance improvements and a new operational dashboard.
In addition, the vendor made generally available InfluxDB Clustered, a self-managed version of its database for on-premises and private cloud deployments first unveiled in September 2023.
Based in San Francisco, InfluxData is a time series database specialist and the creator and lead sponsor of InfluxDB, an open source database designed specifically to manage the data that enables time series analysis.
The vendor raised $81 million in financing in February 2023 to bring its total funding to more than $200 million. Two months later, InfluxData unveiled InfluxDB 3.0. The product suite includes InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated, both of which are managed by InfluxData, and now InfluxDB Clustered as well for self-managed users.
One of the key upgrades in InfluxDB 3.0 was enabling unlimited cardinality, which refers to the uniqueness of the values in a database column — a high level of distinctness means the column has high cardinality.
Other key upgrades included high throughput to enable users to ingest, transform and analyze hundreds of millions of time series data points per second, significantly faster real-time query response times, increased data compression to reduce storage costs and support for SQL to simplify analysis.
The new features add to those that initially comprise InfluxDB 3.0 and are aimed at helping InfluxData stand out in a competitive market, according to IDC analyst Carl Olofson. Other time series database specialists include Grafana and Prometheus, while tech giants AWS, Google, IBM and Microsoft are among others offering time series databases.
“The [keys] are size and speed,” Olofson said. “The time series field has, in recent years, become very competitive. InfluxData is clearly looking to stand out, realizing that as users develop more complex networks of data sources — including edge devices — the challenge of applying a single analysis against all that data is becoming overwhelming.”
New capabilities
Time series data is data that is time stamped so that an enterprise’s changes can be observed over time.
Meanwhile, just as more data sources are resulting in an increase in the overall volume of data enterprises now collect, the number of sources and resulting data volume that enable changes to be tracked over time are also rising.
In response, InfluxData and its peers have developed databases that specialize in managing time series data. Common characteristics of such databases include optimization for large-scale workloads, high-performance reading and writing capabilities to enable real-time analysis, processes for managing data lifecycles so that older data can be retained and found, and filters specific to time-based queries.
InfluxDB 3.0’s initial launch represented a complete overhaul of the database’s underlying engine. Along with the new underlying engine, the release addressed and added some of those common characteristics such as high performance and capabilities to enable real-time analysis.
Now, the latest release of InfluxDB 3.0 is aimed at increasing the database engine’s performance as well as simplifying its use.
Carl OlofsonAnalyst, IDC
The update includes improved query concurrency and scaling to better handle high-cardinality data. In addition, InfluxDB 3.0 now has a new operational dashboard that provides visual insights into the performance and health of data clusters so that developers can address unintended workload changes, identify bottlenecks and optimize performance. A new single sign-on streamlines the log-in process. And new APIs have been added that let users automate certain repetitive tasks.
“High cardinality is the key here,” Olofson said. “You can do time series queries and analysis on much larger data sets with high performance than was possible before.”
Rachel Stephens, an analyst at RedMonk, similarly said that continuing to address cardinality is key for InfluxData.
She noted that time series databases have historically struggled with high cardinality use cases. InfluxDB 3.0’s initial release improved InfluxData’s handling of high-cardinality workloads, with the new release adding further performance.
“InfluxDB 3.0 potentially opens up new space in the market for the database to be a performant option in [high cardinality] situations,” Stephens said.
While the InfluxDB 3.0 update addresses performance, the launch of InfluxDB Clustered extends the database engine’s capabilities to more of the vendor’s users.
When InfluxDB 3.0 was first released, it was available to only users of InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated, which are both fully managed database services. On-premises and private cloud users had only InfluxDB Enterprise — which was not built with InfluxDB 3.0’s engine — as an option.
InfluxDB Clustered essentially replaces InfluxDB Enterprise. Its significance, therefore, is that it provides on-premises and private cloud customers with the same capabilities as users of InfluxData’s fully managed databases, according to Stephens.
“InfluxDB Clustered is the successor product to InfluxDB Enterprise,” she said. “InfluxDB Clustered brings the columnar database engine to customers’ self-managed environments.”
The impetus for the InfluxDB 3.0 improvements and launch of InfluxDB Clustered came from InfluxData’s goal of providing developers tools that allow them to efficiently manage time series workloads at scale, according to Gary Fowler, the vendor’s vice president of products.
In particular, enabling developers to process large data sets in real time is essential, given the increasing demand for real-time decision-making.
“As workloads continue to expand, developers need sophisticated systems that can handle large data sets without compromising performance,” he said. “InfluxDB 3.0 is engineered to meet these challenges head-on, offering the tools necessary to manage time series data at scale.”
In the future
With the full suite of InfluxDB 3.0 products now generally available, InfluxData’s roadmap is focused on continuing to add new features and functionality, according to Fowler.
In addition, Fowler said the vendor is planning to improve the performance of Amazon Timestream for InfluxDB, a managed offering resulting from InfluxData’s partnership with AWS.
Currently, Amazon Timestream for InfluxDB is based on a pre-InfluxDB 3.0 engine, which makes it an option for open source users with small, low cardinality workloads. Now, InfluxData is working to bring InfluxDB 3.0 to Amazon Timestream for InfluxDB along with other features not yet available to open source users.
“These enhancements will provide greater flexibility, performance and security for our users as they manage their time series data in the cloud,” Fowler said.
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
Technology
Twitch will do a better job of telling rulebreakers why their accounts were suspended
TwitchCon San Diego is taking place this weekend and, as always, the platform had some news to share during the opening ceremony. For one thing, Twitch CEO Dan Clancy said the service will offer streamers and viewers who break the rules more clarity over why their accounts were suspended.
Soon, Twitch will share any chat excerpt that led to a suspension with the user in question via email and the appeals portal. Eventually, this will expand to clips, so streamers can see how they were deemed to have broken the rules on a livestream or VOD. “We want to give you this information so that you can see what you did, what policies were violated, and if you feel our decision was incorrect, you can appeal,” Twitch wrote in a blog post.
The service is also aware that permanent strikes on an account can pose a problem for long-time streamers who may eventually get banned for a smaller slip up. To that end, Twitch is bringing in a strike expiration policy starting in early 2025. “Low-severity strikes will no longer put streamers’ livelihoods at risk, but we’ll still enforce the rules for major violations,” Twitch said. “Plus, we’re adding more transparency by showing you exactly what led to a strike.”
On the broadcasting front, viewers of streamers who are using Twitch’s Enhanced Broadcasting feature will be able to watch streams in 2K starting early next year. This option will be available in select regions at first, with Twitch planning to expand it elsewhere throughout 2025. Also of note, Clancy said that “we’re working on 4K.”
Also coming in 2025 is the option for those using Enhanced Broadcasting to stream vertical and landscape video at the same time. The idea here is to offer viewers an optimal experience depending on which device they’re using to watch streams.
Elsewhere, Twitch is planning some improvements to navigation in its overhauled mobile app, such as letting you access your Followed channels with a single swipe and prioritizing audio from the picture-in-picture player. Streamers will have access to a feature called Clip Carousel, which will highlight the best clips from their latest stream and make them easy to share on desktop and mobile. The platform says it’ll be easier for viewers to create clips on mobile devices too.
In addition, Twitch will roll out a shared chat option in the Stream Together feature next week, allowing up to six creators who are streaming together to combine their chats. Streamers’ mods will be able to moderate all of the messages in a shared chat and time out or ban anyone who crosses a line. Creators who hop on a Stream Together session can also turn off Shared Chat for their own community.
Last but not least, Twitch will expand its Unity Guilds and Creator Clubs. The idea behind both is to help streamers forge connections, learn from each other and grow with the help of Twitch staff. Over the last year, Twitch has opened up the Black Guild, Women’s Guild and Hispanic and Latin Guild, and it just announced a Pride Guild for the LGBTQIA+ community. All four guilds will expand to accept members from around the world next year.
Creator Clubs are a newer thing that Twitch debuted last month for the DJ and IRL categories. Twitch says that engagement has been higher than expected. Four more Creator Clubs are coming soon for the Artists/Makers, Music, VTubers and Coworking/Coding categories.
Technology
Top Strategies to Secure Machine Learning Models
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Adversarial attacks on machine learning (ML) models are growing in intensity, frequency and sophistication with more enterprises admitting they have experienced an AI-related security incident.
AI’s pervasive adoption is leading to a rapidly expanding threat surface that all enterprises struggle to keep up with. A recent Gartner survey on AI adoption shows that 73% of enterprises have hundreds or thousands of AI models deployed.
HiddenLayer’s earlier study found that 77% of the companies identified AI-related breaches, and the remaining companies were uncertain whether their AI models had been attacked. Two in five organizations had an AI privacy breach or security incident of which 1 in 4 were malicious attacks.
A growing threat of adversarial attacks
With AI’s growing influence across industries, malicious attackers continue to sharpen their tradecraft to exploit ML models’ growing base of vulnerabilities as the variety and volume of threat surfaces expand.
Adversarial attacks on ML models look to exploit gaps by intentionally attempting to redirect the model with inputs, corrupted data, jailbreak prompts and by hiding malicious commands in images loaded back into a model for analysis. Attackers fine-tune adversarial attacks to make models deliver false predictions and classifications, producing the wrong output.
VentureBeat contributor Ben Dickson explains how adversarial attacks work, the many forms they take and the history of research in this area.
Gartner also found that 41% of organizations reported experiencing some form of AI security incident, including adversarial attacks targeting ML models. Of those reported incidents, 60% were data compromises by an internal party, while 27% were malicious attacks on the organization’s AI infrastructure. Thirty percent of all AI cyberattacks will leverage training-data poisoning, AI model theft or adversarial samples to attack AI-powered systems.
Adversarial ML attacks on network security are growing
Disrupting entire networks with adversarial ML attacks is the stealth attack strategy nation-states are betting on to disrupt their adversaries’ infrastructure, which will have a cascading effect across supply chains. The 2024 Annual Threat Assessment of the U.S. Intelligence Community provides a sobering look at how important it is to protect networks from adversarial ML model attacks and why businesses need to consider better securing their private networks against adversarial ML attacks.
A recent study highlighted how the growing complexity of network environments demands more sophisticated ML techniques, creating new vulnerabilities for attackers to exploit. Researchers are seeing that the threat of adversarial attacks on ML in network security is reaching epidemic levels.
The quickly accelerating number of connected devices and the proliferation of data put enterprises into an arms race with malicious attackers, many financed by nation-states seeking to control global networks for political and financial gain. It’s no longer a question of if an organization will face an adversarial attack but when. The battle against adversarial attacks is ongoing, but organizations can gain the upper hand with the right strategies and tools.
Cisco, Cradlepoint( a subsidiary of Ericsson), DarkTrace, Fortinet, Palo Alto Networks, and other leading cybersecurity vendors have deep expertise in AI and ML to detect network threats and protect network infrastructure. Each is taking a unique approach to solving this challenge. VentureBeat’s analysis of Cisco’s and Cradlepoint’s latest developments indicates how fast vendors address this and other network and model security threats. Cisco’s recent acquisition of Robust Intelligence accentuates how important protecting ML models is to the network giant.
Understanding adversarial attacks
Adversarial attacks exploit weaknesses in the data’s integrity and the ML model’s robustness. According to NIST’s Artificial Intelligence Risk Management Framework, these attacks introduce vulnerabilities, exposing systems to adversarial exploitation.
There are several types of adversarial attacks:
Data Poisoning: Attackers introduce malicious data into a model’s training set to degrade performance or control predictions. According to a Gartner report from 2023, nearly 30% of AI-enabled organizations, particularly those in finance and healthcare, have experienced such attacks. Backdoor attacks embed specific triggers in training data, causing models to behave incorrectly when these triggers appear in real-world inputs. A 2023 MIT study highlights the growing risk of such attacks as AI adoption grows, making defense strategies such as adversarial training increasingly important.
Evasion Attacks: These attacks alter input data to mispredict. Slight image distortions can confuse models into misclassified objects. A popular evasion method, the Fast Gradient Sign Method (FGSM) uses adversarial noise to trick models. Evasion attacks in the autonomous vehicle industry have caused safety concerns, with altered stop signs misinterpreted as yield signs. A 2019 study found that a small sticker on a stop sign misled a self-driving car into thinking it was a speed limit sign. Tencent’s Keen Security Lab used road stickers to trick a Tesla Model S’s autopilot system. These stickers steered the car into the wrong lane, showing how small carefully crafted input changes can be dangerous. Adversarial attacks on critical systems like autonomous vehicles are real-world threats.
Model Inversion: Allows adversaries to infer sensitive data from a model’s outputs, posing significant risks when trained on confidential data like health or financial records. Hackers query the model and use the responses to reverse-engineer training data. In 2023, Gartner warned, “The misuse of model inversion can lead to significant privacy violations, especially in healthcare and financial sectors, where adversaries can extract patient or customer information from AI systems.”
Model Stealing: Repeated API queries are used to replicate model functionality. These queries help the attacker create a surrogate model that behaves like the original. AI Security states, “AI models are often targeted through API queries to reverse-engineer their functionality, posing significant risks to proprietary systems, especially in sectors like finance, healthcare, and autonomous vehicles.” These attacks are increasing as AI is used more, raising concerns about IP and trade secrets in AI models.
Recognizing the weak points in your AI systems
Securing ML models against adversarial attacks requires understanding the vulnerabilities in AI systems. Key areas of focus need to include:
Data Poisoning and Bias Attacks: Attackers target AI systems by injecting biased or malicious data, compromising model integrity. Healthcare, finance, manufacturing and autonomous vehicle industries have all experienced these attacks recently. The 2024 NIST report warns that weak data governance amplifies these risks. Gartner notes that adversarial training and robust data controls can boost AI resilience by up to 30%. Implementing secure data pipelines and constant validation is essential to protecting critical models.
Model Integrity and Adversarial Training: Machine learning models can be manipulated without adversarial training. Adversarial training uses adverse examples and significantly strengthens a model’s defenses. Researchers say adversarial training improves robustness but requires longer training times and may trade accuracy for resilience. Although flawed, it is an essential defense against adversarial attacks. Researchers have also found that poor machine identity management in hybrid cloud environments increases the risk of adversarial attacks on machine learning models.
API Vulnerabilities: Model-stealing and other adversarial attacks are highly effective against public APIs and are essential for obtaining AI model outputs. Many businesses are susceptible to exploitation because they lack strong API security, as was mentioned at BlackHat 2022. Vendors, including Checkmarx and Traceable AI, are automating API discovery and ending malicious bots to mitigate these risks. API security must be strengthened to preserve the integrity of AI models and safeguard sensitive data.
Best practices for securing ML models
Implementing the following best practices can significantly reduce the risks posed by adversarial attacks:
Robust Data Management and Model Management: NIST recommends strict data sanitization and filtering to prevent data poisoning in machine learning models. Avoiding malicious data integration requires regular governance reviews of third-party data sources. ML models must also be secured by tracking model versions, monitoring production performance and implementing automated, secured updates. BlackHat 2022 researchers stressed the need for continuous monitoring and updates to secure software supply chains by protecting machine learning models. Organizations can improve AI system security and reliability through robust data and model management.
Adversarial Training: ML models are strengthened by adversarial examples created using the Fast Gradient Sign Method (FGSM). FGSM adjusts input data by small amounts to increase model errors, helping models recognize and resist attacks. According to researchers, this method can increase model resilience by 30%. Researchers write that “adversarial training is one of the most effective methods for improving model robustness against sophisticated threats.”
Homomorphic Encryption and Secure Access: When safeguarding data in machine learning, particularly in sensitive fields like healthcare and finance, homomorphic encryption provides robust protection by enabling computations on encrypted data without exposure. EY states, “Homomorphic encryption is a game-changer for sectors that require high levels of privacy, as it allows secure data processing without compromising confidentiality.” Combining this with remote browser isolation further reduces attack surfaces ensuring that managed and unmanaged devices are protected through secure access protocols.
API Security: Public-facing APIs must be secured to prevent model-stealing and protect sensitive data. BlackHat 2022 noted that cybercriminals increasingly use API vulnerabilities to breach enterprise tech stacks and software supply chains. AI-driven insights like network traffic anomaly analysis help detect vulnerabilities in real time and strengthen defenses. API security can reduce an organization’s attack surface and protect AI models from adversaries.
Regular Model Audits: Periodic audits are crucial for detecting vulnerabilities and addressing data drift in machine learning models. Regular testing for adversarial examples ensures models remain robust against evolving threats. Researchers note that “audits improve security and resilience in dynamic environments.” Gartner’s recent report on securing AI emphasizes that consistent governance reviews and monitoring data pipelines are essential for maintaining model integrity and preventing adversarial manipulation. These practices safeguard long-term security and adaptability.
Technology solutions to secure ML models
Several technologies and techniques are proving effective in defending against adversarial attacks targeting machine learning models:
Differential privacy: This technique protects sensitive data by introducing noise into model outputs without appreciably lowering accuracy. This strategy is particularly crucial for sectors like healthcare that value privacy. Differential privacy is a technique used by Microsoft and IBM among other companies to protect sensitive data in their AI systems.
AI-Powered Secure Access Service Edge (SASE): As enterprises increasingly consolidate networking and security, SASE solutions are gaining widespread adoption. Major vendors competing in this space include Cisco, Ericsson, Fortinet, Palo Alto Networks, VMware and Zscaler. These companies offer a range of capabilities to address the growing need for secure access in distributed and hybrid environments. With Gartner predicting that 80% of organizations will adopt SASE by 2025 this market is set to expand rapidly.
Ericsson distinguishes itself by integrating 5G-optimized SD-WAN and Zero Trust security, enhanced by acquiring Ericom. This combination enables Ericsson to deliver a cloud-based SASE solution tailored for hybrid workforces and IoT deployments. Its Ericsson NetCloud SASE platform has proven valuable in providing AI-powered analytics and real-time threat detection to the network edge. Their platform integrates Zero Trust Network Access (ZTNA), identity-based access control, and encrypted traffic inspection. Ericsson’s cellular intelligence and telemetry data train AI models that aim to improve troubleshooting assistance. Their AIOps can automatically detect latency, isolate it to a cellular interface, determine the root cause as a problem with the cellular signal and then recommend remediation.
Federated Learning with Homomorphic Encryption: Federated learning allows decentralized ML training without sharing raw data, protecting privacy. Computing encrypted data with homomorphic encryption ensures security throughout the process. Google, IBM, Microsoft, and Intel are developing these technologies, especially in healthcare and finance. Google and IBM use these methods to protect data during collaborative AI model training, while Intel uses hardware-accelerated encryption to secure federated learning environments. Data privacy is protected by these innovations for secure, decentralized AI.
Defending against attacks
Given the potential severity of adversarial attacks, including data poisoning, model inversion, and evasion, healthcare and finance are especially vulnerable, as these industries are favorite targets for attackers. By employing techniques including adversarial training, robust data management, and secure API practices, organizations can significantly reduce the risks posed by adversarial attacks. AI-powered SASE, built with cellular-first optimization and AI-driven intelligence has proven effective in defending against attacks on networks.
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Technology
Adam Neumann’s startup Flow opens co-living community in Saudi Arabia
Flow, Adam Neumann’s co-living startup, opened a compound with 238 apartments in Saudi Arabia’s capital, Riyadh, and Forbes has some details. The opening included an Aztec-themed hot chocolate ceremony and tote bags with the words “holy s— I’m alive” on them. The rent for the furnished units starts at $3,500 a month and includes hotel-style services such as laundry and housekeeping and amenities like pools, co-ed gyms (unusual in Saudi Arabia), and bowling alleys. Flow is building three other properties with nearly 1,000 apartments in Riyadh.
The company’s first but less luxurious properties were opened in Fort Lauderdale and Miami in April.
Flow raised $350 million from Andreessen Horowitz in 2022. The funding raised eyebrows given the problematic history of Neumann’s previous startup, WeWork. Once valued at $47 billion, WeWork filed for bankruptcy protection last year and was ultimately acquired by Yardi, a real estate group, for $450 million.
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