Apple is rolling out a new “Stolen Device Protection” feature that seems well thought out:

When Stolen Device Protection is turned on, Face ID or Touch ID authentication is required for additional actions, including viewing passwords or passkeys stored in iCloud Keychain, applying for a new Apple Card, turning off Lost Mode, erasing all content and settings, using payment methods saved in Safari, and more. No passcode fallback is available in the event that the user is unable to complete Face ID or Touch ID authentication.

For especially sensitive actions, including changing the password of the Apple ID account associated with the iPhone, the feature adds a security delay on top of biometric authentication. In these cases, the user must authenticate with Face ID or Touch ID, wait one hour, and authenticate with Face ID or Touch ID again. However, Apple said there will be no delay when the iPhone is in familiar locations, such as at home or work.

More details at the link.

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The content of this post is solely the responsibility of the author.  AT&T does not adopt or endorse any of the views, positions, or information provided by the author in this article. 

Beyond ‘just’ causing mayhem in the outside world, the pandemic also led to a serious and worrying rise in cybersecurity breaches. In 2020 and 2021, businesses saw a whopping 50% increase in the amount of attempted breaches.

The transition to remote work, outdated healthcare organization technology, the adoption of AI bots in the workplace, and the presence of general uncertainty and fear led to new opportunities for bad actors seeking to exploit and benefit from this global health crisis.

In this article, we will take a look at how all of this impacts the state of cybersecurity in the current post-pandemic era, and what conclusions can be drawn.

New world, new vulnerabilities

Worldwide lockdowns led to a rise in remote work opportunities, which was a necessary adjustment to allow employees to continue to earn a living. However, the sudden shift to the work-from-home format also caused a number of challenges and confusion for businesses and remote employees alike.

The average person didn’t have the IT department a couple of feet away, so they were forced to fend for themselves. Whether it was deciding whether to use a VPN or not, was that email really a phishing one, or even just plain software updates, everybody had their hands full.

With employers busy with training programs, threat actors began intensifying their ransomware-related efforts, resulting in a plethora of high-profile incidents in the last couple of years.

A double-edged digital sword

If the pandemic did one thing, it’s making us more reliant on both software and digital currencies. You already know where we’re going with this—it’s fertile ground for cybercrime.

Everyone from the Costa Rican government to Nvidia got hit. With the dominance of Bitcoin as a payment method in ransoming, tracking down perpetrators is infinitely more difficult than it used to be. The old adage holds more true than ever – an ounce of prevention is worth a pound of cure.

To make matters worse, amongst all that chaos, organizations also had to pivot away from vulnerable, mainstream software solutions. Even if it’s just choosing a new image editor or integrating a PDF SDK, it’s an increasing burden for businesses that are already trying to modernize or simply maintain.

Actors strike where we’re most vulnerable

Healthcare organizations became more important than ever during the global coronavirus pandemic. But this time also saw unprecedented amounts of cybersecurity incidents take place as bad actors exploited outdated cybersecurity measures.

The influx of sudden need caused many overburdened healthcare organizations to lose track of key cybersecurity protocols that could help shore up gaps in the existing protective measures.

The United States healthcare industry saw a 25% spike in successful data breaches during the pandemic, which resulted in millions of dollars of damages and the loss of privacy for thousands of patients whose data was compromised.

This has resulted in intangible lasting damages as well – patients today have much greater reservations when it comes to trusting that the information they share with their healthcare organizations is secure.

Healthcare organizations need to update their existing cybersecurity systems, both physical and digital, to accommodate new technological innovations. Patient data must be amply secured through zero trust networks and multi-factor authorizations that ensure that only verified users can access their records within the system.

Healthcare organizations should put in place layered cybersecurity systems that include emergency response plans for mitigating damages and leaked data access points in the event of a successful data breach.

Cybersecurity training and awareness education should be compulsory for all employees of any healthcare organization. When it comes to healthcare, trust is absolutely essential, and that includes trust in an organization to protect patient data and privacy in a sufficiently secure manner. Healthcare organizations should also ensure that their security measures and protocols are compliant with HIPAA and other federal regulations.

Learned to exploit people’s anxiety

Misinformation, a frightening news cycle, and a sudden burst of communication from official channels meant that during the pandemic, many individuals were highly susceptible to insidious phishing attacks that relied on social engineering cyberattack techniques.

Bad actors impersonating public figures, misrepresenting national entities, or falsely presenting as employees from healthcare companies or social security firms could more easily ingratiate themselves with unsuspecting individuals, who could then be extorted into providing sensitive personal details, such as physical address, credit card information, bank details, confidential health information, and more.

In fact, studies have since revealed that instances of phishing attacks rose by a staggering 220% during the pandemic. These phishing attacks resulted in unmeasurable amounts of damage, as individuals were coerced or tricked into handing over money and data that could then be used against them. Unsuspecting victims could fall prey to synthetic identity fraud or ransomware attacks, among others.

Going forward, we need broad public awareness campaigns that can alert individuals to the dangers and likelihood of phishing attacks. People should be aware of what familiar phishing attacks look like, including specific scripts that are all too common in email phishing attacks.

Biggest threat might not even be human

Now, in the post-pandemic era, we are squarely placed in the realm of AI. When ChatGPT was released to the public it broke records, gaining over 100 million users within its first two months of existence.

Now, experts predict that AI will replace between 400 and 800 million jobs. That means businesses in every sphere will become even more enmeshed in AI technology. If we are not careful, then that also means businesses will be highly susceptible to new forms of cyberattack.

Bad actors can enact AI-driven cyberattacks to turn new AI tech against organizations. Or they could easily exploit a vulnerability in a faulty AI model.

From the past to prepare for the future

With the climate crisis looming and healthcare experts predicting that the next pandemic is going to be even worse, it is vital that we are prepared. Cybercriminals love turbulent periods, and it’s best we took our pandemic lessons to heart.

Learning from the cybersecurity crises that erupted as a result of the last pandemic is a smart way to approach the uncertainties of the future.

Taking the past issues as a starting point, we can analyze what went wrong, from the dangers of new remote work vulnerabilities that resulted from the sudden shift away from the office during the pandemic to data breaches of healthcare systems that relied on outdated technology.

The mistakes of the recent past can help us shore up cybersecurity across the board so that we can be better prepared to face the future, with whatever global challenges it may bring.

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The content of this post is solely the responsibility of the author.  AT&T does not adopt or endorse any of the views, positions, or information provided by the author in this article. 

In the intricate world of artificial intelligence, it’s essential to distinguish between the different AI technologies at our disposal. Two key domains that often lead to confusion are conversational AI and generative AI.

Though their names might sound related, they are fundamentally different in their applications and underlying mechanisms. Let’s dive into the realm of AI to elucidate the distinctions between these two intriguing domains.

Exploring generative AI

Definition and key characteristics: generative AI is all about creativity and content generation. It differs significantly from Conversational AI in that it is primarily focused on creating new, original content.

The hallmark of generative AI is its ability to generate content autonomously by learning patterns from extensive datasets.

Whether it’s crafting textual content, synthesizing images, composing music, even creating entire apps, generative AI thrives in producing innovative material without direct human input.

This technology operates on intricate deep learning architectures, often employing advanced techniques like generative adversarial networks (GANs) and autoregressive models to create content independently, showcasing its creative potential.

Applications: generative AI finds its niche in a broad spectrum of creative endeavours. From art and design to data synthesis and content generation, its applications are diverse and ever-expanding.

For instance, AI algorithms can produce unique artworks, deepfake videos, or even generate entire articles, demonstrating a wide range of creative possibilities.

It’s a boon for artists, designers, and content creators looking to harness the power of AI to augment their work or generate new, innovative content, enabling humans to explore new frontiers of creativity and content generation, making it an exciting domain within the AI landscape.

Understanding conversational AI

Definition and core features: conversational AI is a technology tailored for human-like interactions, aiming to facilitate conversations with users. It relies heavily on natural language processing (NLP) and dialogue systems.

These systems excel at interpreting human language and responding appropriately. When you engage with chatbots, virtual assistants, or even customer service chat interfaces, you are essentially interacting with conversational AI.

The magic behind conversational AI often revolves around predefined responses, rule-based algorithms, and occasionally, machine learning models to understand and generate contextually relevant replies.

Applications: conversational AI primarily finds its applications in customer support, virtual assistants, and communication platforms. Its primary mission is to mimic human conversation, providing users with a seamless and efficient communication experience.

For example, customer support chatbots can answer inquiries, guide users, and handle common issues, all while emulating a human-like interaction. This makes conversational AI indispensable in various industries where human interaction plays a crucial role.

Key differences between conversational and generative AI

Data input and output: The primary divergence between these two domains lies in data input and output. Conversational AI focuses on understanding and responding to human input, aiming to provide interactive dialogue. Generative AI, conversely, takes diverse data inputs and excels in generating entirely new content, showcasing its creative capabilities.

Use cases and applications: Conversational AI predominantly serves in customer support, enhancing user experiences, and ensuring efficient communication. Generative AI extends its reach to content creation, enriching artistic expression, and autonomously generating diverse forms of content.

Underlying models and techniques: conversational AI leans on NLP and dialogue systems, allowing it to comprehend and respond contextually to user queries. Generative AI harnesses the power of deep learning models, GANs, and autoregressive techniques to create content independently of direct human interaction.

Interaction with humans: Conversational AI is designed to mimic human conversation patterns, striving to engage users in interactive dialogues and problem-solving. In contrast, Generative AI operates autonomously, producing content without the need for direct human interaction, thereby showcasing its ability to create original material.

Real-world examples

Here are a couple of familiar examples of generative vs conversational AI.

Conversational AI

When you think of conversational AI, envision virtual assistants like Siri, Google Assistant, or Amazon’s Alexa. These digital companions are designed to engage in responsive conversations, answer questions, set reminders, and even control smart home devices.

They excel at interpreting natural language and providing immediate responses. Siri, for instance, can help you with tasks like finding information on the internet, sending messages, or even telling you a joke. These virtual assistants are prime examples of conversational AI in action, providing a seamless and interactive experience for users.

Generative AI

On the other hand, generative AI showcases its creative potential in diverse ways. Consider the world of art where AI algorithms create unique and sometimes abstract artworks, pushing the boundaries of artistic expression. Deepfake videos, another example of generative AI, blend facial reenactment with AI-generated content, enabling the creation of realistic video manipulations.

These practical applications demonstrate the incredible creative and content-generating abilities of generative AI, expanding the horizons of what AI can accomplish in the realm of creativity and content production.

Challenges and ethical considerations

Both conversational and generative AI confront unique challenges. Conversational AI must ensure unbiased responses and fair treatment to all users, as biases can inadvertently creep into responses.

Generative AI grapples with preserving privacy and preventing the misuse of its creative potential, particularly in deepfake and misinformation scenarios that are readily exploited by threat actors.

The ethical considerations of AI development, such as its impact on employment and implications for creativity and originality, are relevant in both domains.

The future of conversational and generative AI

As we look to the future, conversational AI is set to evolve by becoming more context-aware, enhancing customer experiences, and ensuring even more nuanced interactions. Generative AI will continue to redefine creativity across a spectrum of fields, offering advancements in artistic expression, content generation, and innovation.

The synergies that can potentially emerge between these two domains present exciting opportunities in reshaping AI-driven human interactions.

In the vast landscape of artificial intelligence, both conversational AI and generative AI play pivotal roles. While conversational AI enriches our interactive experiences, generative AI unleashes boundless creative possibilities. By understanding these differences, you gain insight into the diverse world of AI, empowering you to navigate the digital landscape with a discerning eye.

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