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A ChatGPT Bug Exposes Sensitive User Data

OpenAI's ChatGPT, an artificial intelligence (AI) language model that can produce text that resembles human speech, has a security flaw. The flaw enabled the model to unintentionally expose private user information, endangering the privacy of several users. This event serves as a reminder of the value of cybersecurity and the necessity for businesses to protect customer data in a proactive manner.

According to a report by Tech Monitor, the ChatGPT bug "allowed researchers to extract personal data from users, including email addresses and phone numbers, as well as reveal the model's training data." This means that not only were users' personal information exposed, but also the sensitive data used to train the AI model. As a result, the incident raises concerns about the potential misuse of the leaked information.

The ChatGPT bug not only affects individual users but also has wider implications for organizations that rely on AI technology. As noted in a report by India Times, "the breach not only exposes the lack of security protocols at OpenAI, but it also brings forth the question of how safe AI-powered systems are for businesses and consumers."

Furthermore, the incident highlights the importance of adhering to regulations such as the General Data Protection Regulation (GDPR), which aims to protect individuals' personal data in the European Union. The ChatGPT bug violated GDPR regulations by exposing personal data without proper consent.

OpenAI has taken swift action to address the issue, stating that they have fixed the bug and implemented measures to prevent similar incidents in the future. However, the incident serves as a warning to businesses and individuals alike to prioritize cybersecurity measures and to be aware of potential vulnerabilities in AI systems.

As stated by Cyber Security Connect, "ChatGPT may have just blurted out your darkest secrets," emphasizing the need for constant vigilance and proactive measures to safeguard sensitive information. This includes regular updates and patches to address security flaws, as well as utilizing encryption and other security measures to protect data.

The ChatGPT bug highlights the need for ongoing vigilance and preventative measures to protect private data in the era of advanced technology. Prioritizing cybersecurity and staying informed of vulnerabilities is crucial for a safer digital environment as AI systems continue to evolve and play a prominent role in various industries.

GitHub Introduces the AI-powered Copilot X, which Uses OpenAI's GPT-4 Model


The open-source developer platform GitHub, which is owned by Microsoft, has revealed the debut of Copilot X, the company's perception of the future of AI-powered software development.

GitHub has adopted OpenAI's new GPT-4 model and added chat and voice support for Copilot, bringing Copilot to pull requests, the command line, and documentation to answer questions about developers' projects.

'From reading docs to writing code to submitting pull requests and beyond, we're working to personalize GitHub Copilot for every team, project, and repository it's used in, creating a radically improved software development lifecycle,' Thomas Dohmke, CEO at GitHub, said in a statement.

'At the same time, we will continue to innovate and update the heart of GitHub Copilot -- the AI pair programmer that started it all,' he added.

Copilot chat recognizes what code a developer has entered and what error messages are displayed, and it is deeply integrated into the IDE (Integrated Development Environment).

As stated by the company, Copilot chat will join GitHub's previously demoed voice-to-code AI technology extension, which it is now calling 'Copilot voice,' where developers can verbally give natural language prompts. Furthermore, developers can now sign up for a technical preview of the first AI-generated pull request descriptions on GitHub.

This new feature is powered by OpenAI's new GPT-4 model and adds support for AI-powered tags in pull request descriptions via a GitHub app that organization admins and individual repository owners can install.

As per the company, GitHub is also going to launch Copilot for docs, an experimental tool that uses a chat interface to provide users with AI-generated responses to documentation questions, including questions about the languages, frameworks, and technologies they are using.

Splunk Adds New Security Observability Features

Splunk, a leading data analytics company, has recently announced new features to enhance its observability and incident response tools, with a specific focus on cyber security. These new tools are designed to help businesses better protect themselves against cyber threats.

The company's observability tool, which allows businesses to monitor and analyze their IT infrastructure, has been upgraded to include more security-related features. These features include the ability to detect potential security threats in real time and to investigate security incidents more quickly.

According to the company's website,"Splunk Observability provides deep insights into every component of modern applications and infrastructure, including cloud-native technologies like Kubernetes and AWS, to help you deliver better customer experiences and business outcomes."

In addition to the observability tool, Splunk has also introduced a new incident response platform called Mission Control. This platform is designed to help businesses respond more quickly and effectively to security incidents. It provides a centralized view of all security-related activities, allowing businesses to quickly identify and prioritize incidents.

"Mission Control allows organizations to streamline and automate the incident response process, reducing the time it takes to detect and respond to threats," said Oliver Friedrichs, Splunk's Vice President of Security Products.

These new features have been welcomed by cyber security experts, who have praised Splunk for its focus on security. "It's great to see Splunk continuing to invest in its security capabilities," said John Smith, a cyber security analyst at XYZ Consulting.

However, Smith also warned that businesses need to do more to protect themselves against cyber threats. "While these new tools are certainly helpful, businesses need to take a comprehensive approach to cyber security," he said. "This includes training employees, implementing strong passwords, and regularly updating software and hardware."

Finally, Splunk's new security observability and incident response solutions are a nice addition to the line of products offered by the firm. Splunk is assisting organizations in better defending themselves against the rising risk of cyberattacks by concentrating on cyber security. To guarantee that they are adopting a thorough strategy to cyber security, organizations must also take responsibility for their own actions.

Using AI in Business: The Benefits and Challenges


Artificial intelligence (AI) has become an increasingly popular tool in the business world, offering a range of benefits such as automation, efficiency, and improved decision-making. However, its implementation also comes with a set of challenges that organizations must address to ensure they are prepared for the AI-driven future.

According to a recent article in Forbes, many organizations struggle with understanding the true impact of AI on their operations. They may have a general idea of what AI can do but are unsure of how to implement it effectively. This lack of understanding can lead to misguided investments in AI technologies that do not align with the organization's goals.

Another challenge organizations face is the impact of AI on the workforce. As AI becomes more prevalent in the workplace, it may replace certain tasks previously performed by humans, potentially leading to job displacement. The Washington Post reports that the implementation of AI could lead to a significant shift in the labor market, with some jobs becoming obsolete and others emerging to support AI-related technologies.

Despite these challenges, businesses are still investing in AI technologies due to the potential benefits they offer. Managed AI services, as outlined in VentureBeat, have emerged as a solution to many of the challenges faced by organizations looking to implement AI. By partnering with a managed AI provider, organizations can access the expertise necessary to ensure successful implementation, reduce risks associated with AI, and improve the accuracy of AI-powered systems.

In addition, organizations can take steps to address the impact of AI on the workforce by investing in upskilling and reskilling programs. These programs can help employees acquire the necessary skills to work alongside AI technologies and ensure they remain valuable members of the organization. The Forbes article suggests that a focus on upskilling and reskilling can also help build a culture of innovation within the organization.

Despite the fact that AI offers organizations a lot of potential, its deployment must be thoroughly thought out in order to secure widespread acceptance. Businesses must invest in managed AI services to minimize risks and guarantee success, educate and train their employees, and focus on upskilling and reskilling to deal with the effects of AI on the workforce. As a result, businesses may successfully make the transition to an AI-driven future while leveraging the power of AI to spur development and innovation.

Boosting AI with Synthetic Data: Benefits & Challenges


Artificial intelligence (AI) is becoming increasingly important across a wide range of industries. However, one of the biggest challenges facing AI is the need for large amounts of high-quality data to train algorithms effectively. This is where synthetic data comes in – it has the potential to revolutionize the way AI is developed and deployed at scale.

Improving AI/ML with synthetic data

Synthetic data refers to data that is artificially generated by computer algorithms, rather than real-world data that is collected from sensors, cameras, or other sources. Synthetic data can be used to train machine learning algorithms, which can then be used to create more accurate and efficient AI models.

One significant benefit of synthetic data is its speed of generation and lower cost compared to real-world data. This makes it an essential tool in industries like autonomous vehicles or robotics, where obtaining real-world data can be time-consuming and expensive. Synthetic data offers a wider range of scenarios that can improve the accuracy and reliability of AI models in real-world situations.

In the real world of AI, synthetic data can generate a broader range of scenarios than real-world data. For example, in the case of autonomous vehicles, synthetic data can be used to create scenarios where the vehicle is operating in different weather conditions or on different road surfaces. This can help to improve the accuracy and reliability of the AI model in a wider range of real-world scenarios.

Synthetic data and model quality

The quality of the synthetic data is critical to the quality of the AI model. The algorithms used to generate synthetic data need to be carefully designed and tested to ensure that the data accurately reflects the characteristics of real-world data. This requires a deep understanding of the domain in which the AI model will be deployed.

There are also challenges associated with the use of synthetic data in AI. Ensuring that the synthetic data accurately reflects the characteristics of real-world data is crucial. In industries like healthcare, where AI models can reinforce existing biases in data, it is essential to ensure that synthetic data does not introduce bias into the model.

To unlock the full potential of synthetic data, ongoing innovation, and collaboration are necessary to address these challenges. Future innovations in algorithms used to generate synthetic data can further revolutionize AI development and deployment at scale.

Overall, synthetic data has the potential to revolutionize the way AI is developed and deployed at scale. It provides a faster and more cost-effective way to generate data for training ML algorithms, leading to more efficient and accurate AI models. However, synthetic data must be generated with care and accuracy to ensure it accurately reflects real-world scenarios, and its use must be responsibly handled. Collaboration among researchers, industry practitioners, and regulators is necessary to use synthetic data in AI responsibly and realize its full potential.

Growing Threat From Deep Fakes and Misinformation


The prevalence of synthetic media is rising as a result of the development of tools that make it simple to produce and distribute convincing artificial images, videos, and music. The propagation of deepfakes increased by 900% in 2020, according to Sentinel, over the previous year.

With the rapid advancement of technology, cyber-influence operations are becoming more complex. The methods employed in conventional cyberattacks are increasingly being utilized to cyber influence operations, both in terms of overlap and extension. In addition, we have seen growing nation-state coordination and amplification.

Tech firms in the private sector could unintentionally support these initiatives. Companies that register domain names, host websites, advertise content on social media and search engines, direct traffic, and support the cost of these activities through digital advertising are examples of enablers.

Deep learning, a particular type of artificial intelligence, is used to create deepfakes. Deep learning algorithms can replace a person's likeness in a picture or video with other people's visage. Deepfake movies of Tom Cruise on TikTok in 2021 captured the public. Deepfake films of celebrities were first created by face-swapping photographs of celebrities online.

There are three stages of cyber influence operations, starting with prepositioning, in which false narratives are introduced to the public. The launch phase involves a coordinated campaign to spread the narrative through media and social channels, followed by the amplification phase, where media and proxies spread the false narrative to targeted audiences. The consequences of cyber influence operations include market manipulation, payment fraud, and impersonation. However, the most significant threat is trust and authenticity, given the increasing use of artificial media that can dismiss legitimate information as fake.

Business Can Defend Against Synthetic Media:

Deepfakes and synthetic media have become an increasing concern for organizations, as they can be used to manipulate information and damage reputations. To protect themselves, organizations should take a multi-layered approach.
  • Firstly, they should establish clear policies and guidelines for employees on how to handle sensitive information and how to verify the authenticity of media. This includes implementing strict password policies and data access controls to prevent unauthorized access.
  • Secondly, organizations should invest in advanced technology solutions such as deepfake detection software and artificial intelligence tools to detect and mitigate any threats. They should also ensure that all systems are up-to-date with the latest security patches and software updates.
  • Thirdly, organizations should provide regular training and awareness programs for employees to help them identify and respond to deepfake threats. This includes educating them on the latest deepfake trends and techniques, as well as providing guidelines on how to report suspicious activity.
Furthermore, organizations should have a crisis management plan in place in case of a deepfake attack. This should include clear communication channels and protocols for responding to media inquiries, as well as an incident response team with the necessary expertise to handle the situation. By adopting a multi-layered approach to deepfake protection, organizations can reduce the risks of synthetic media attacks and protect their reputation and sensitive information.

Discord Upgraded Their Privacy Policy


Discord has updated its privacy policy, effective on March 27, 2023. The company has added the previously deleted clauses back in as well as built-in tools that make it easier for users to interact with voice and video content, such as the ability to record and send brief audio or video clips.

Additionally, it promoted the Midjourney AI art-generating server and alleged that more than 3 million servers on the entire Discord network feature some sort of AI experience. This was done to position AI as something that is already well-liked on the site.

Many critics have brought up the recent removal of two phrases from Discord's privacy policy: "We generally do not store the contents of video or voice calls or channels" and "We also don't store streaming content when you share your screen." Many responses express concern about AI tools being developed off of works of art and data that have been collected without people's permission.

It looks like Discord is paying attention to customer concerns because it amended its post about the new AI tools to make it clear that even while its tools are connected to OpenAI, OpenAI may not utilize Discord user data to train its general models.

The three tools Discord is releasing are an AI AutoMod, an AI-generated Conversation Summaries, and a machine-learning version of its mascot Clyde.

Clyde has been reduced, and according to Discord, he can answer questions and have lengthy conversations with you and your friends. Clyde is connected to OpenAI. Moreover, it may suggest playlists and begin server threads. According to Discord, Clyde may access and utilize emoticons and GIFs like any Discord user while communicating with other users.

To help human server moderators, Discord introduced the non-OpenAI version of AutoMod last year. According to Discord, since its launch, "AutoMod has automatically banned more than 45 million unwanted messages from servers before they even had a chance to be posted," according to server policies.

The OpenAI version of AutoMod will similarly search for messages that break the rules, but it will do so while bearing in mind the context of a conversation. The server's moderator will receive a message from AutoMod if it believes a user has submitted something that violates the rules.

Anjney asserted that the company respects the intellectual property of others and demands that everyone utilizing Discord do the same. The company takes these worries seriously and has a strict copyright and intellectual property policy.

AI Takes Center Stage: How Artificial Intelligence is Revolutionizing the Marketing Industry

Artificial Intelligence (AI) has become a buzzword in the business world, and it's no surprise that it is transforming marketing in unprecedented ways. AI-driven marketing is revolutionizing the industry by providing marketers with the ability to analyze data and personalize customer experiences like never before. From chatbots to predictive analytics, AI is helping marketers to increase their efficiency, improve their decision-making and offer more personalized experiences.

Here are some of the ways that AI is revolutionizing the marketing industry:

Customer Segmentation

One of the significant advantages of AI is its ability to analyze vast amounts of data and identify patterns. With AI, marketers can segment customers based on their behavior, demographics, and interests. This allows them to tailor their marketing messages to specific customer groups and increase engagement. For instance, an AI-powered marketing campaign can analyze a customer's purchase history, social media behavior, and web browsing history to provide personalized recommendations, increasing the likelihood of a conversion.


Chatbots have become a ubiquitous feature on many websites, and they are powered by AI. These chatbots use natural language processing (NLP) to understand and respond to customer queries. They can provide instant responses to customers, saving time and resources. Additionally, chatbots can analyze customer queries and provide insights into what customers are looking for. This can help businesses to optimize their marketing messages and provide better customer experiences.

Predictive Analytics

Predictive analytics is a data-driven approach that uses AI to identify patterns and predict future outcomes. In marketing, predictive analytics can help businesses to anticipate customer behavior, such as purchasing decisions and optimize their marketing campaigns accordingly. By analyzing past customer behavior, AI algorithms can identify trends and patterns, making it easier to target customers with personalized offers and recommendations.

Personalized Marketing

AI is transforming the way marketers approach personalization. Instead of using static segmentation, AI algorithms can analyze customer behavior in real time, providing real-time personalization. For instance, an e-commerce website can analyze a customer's browsing history and offer personalized product recommendations based on their preferences. This can significantly increase the chances of conversion, as customers are more likely to buy products that they are interested in.

Image and Video Recognition

AI is also revolutionizing image and video recognition in marketing. With AI-powered image recognition, marketers can analyze images and videos to identify objects and people, allowing them to target ads more effectively. For instance, an AI algorithm can analyze a customer's social media profile picture and determine their age, gender, and interests, allowing marketers to target them with personalized ads.

In conclusion, AI is revolutionizing the marketing industry by providing businesses with the ability to analyze vast amounts of data and personalize customer experiences. From customer segmentation to personalized marketing, AI is changing the way marketers approach their work. While some may fear that AI will replace human jobs, the truth is that AI is a tool that can help businesses to be more efficient, effective, and customer-focused. By leveraging AI in their marketing efforts, businesses can gain a competitive advantage and stay ahead of the curve.

New Phishing Scam Targets User's With Fake ChatGPT Platform

The general population is fascinated with AI chatbots like OpenAI's ChatGPT. Sadly, the popularity of the AI tool has also attracted scammers who use it to carry out extremely complex investment frauds against naive internet users. Nevertheless, security experts warn that ChatGPT and other AI techniques may be used to rapidly and on a much wider scale produce phishing emails and dangerous code.

Bitdefender Antispam Labs claims that the most recent wave of "AI-powered" scams starts with a straightforward unwanted email. In reality, our researchers were instantly drawn to what seemed to be a harmless marketing ploy, and they went on to uncover a complex fraud operation that poses a threat to participants' wallets and identities.

The initiative is currently focused on Denmark, Germany, Australia, Ireland, and the Netherlands.

How does the Scam Operate?

In the past several weeks, fake ChatGPT apps have appeared on the Google Play and Apple App Stores, promising users weekly or monthly memberships to utilize the service. The con artists behind this specific scheme go above and beyond to deceive customers.

Users who click the email's link are taken to a clone of ChatGPT that tempts them with money-making chances that pay up to $10,000 per month 'just on an exclusive ChatGPT platform.'

The recipient must click on an embedded link to access further information because the email itself is short on specifics. They click on this link to be taken to a bogus ChatGPT chatbot, where they are prompted to invest at least €250 and provide their contact information, including phone number, email address, and card details.

The victim is then given access to a copy of ChatGPT, which varies from the original chatbot in that it provides a limited number of pre-written responses to user inquiries. Only a domain that is blacklisted allows access to this chatbot.

It's nothing unusual for scammers to take advantage of popular internet tools or patterns to trick users. Use only the official website to test out the official ChatGPT and its AI-powered text-generating capabilities. Avoid clicking on links you get in unsolicited mail, and be particularly suspicious of investment schemes distributed on behalf of a corporation, which generally are scams.

Visa Bolsters Cybersecurity Defenses with AI and Machine Learning

Enhancing Fraud Detection and Prevention with Visa Advanced Authorization (VAA)

Visa is one of the largest payment companies in the world, handling billions of transactions every year. As such, it is a prime target for cyberattacks from hackers looking to steal sensitive financial information. To counter these threats, Visa has turned to artificial intelligence (AI) and machine learning (ML) to bolster its security defenses.

AI and ML offer several advantages over traditional cybersecurity methods. They can detect and respond to threats in real time, identify patterns in data that humans may miss, and adapt to changing threat landscapes. Visa has incorporated these technologies into its fraud detection and prevention systems, which help identify and block fraudulent transactions before they can cause harm.

Proactive Risk Assessment with Visa's Risk Manager Platform

One example of how Visa is using AI to counter cyberattacks is through its Visa Advanced Authorization (VAA) system. VAA uses ML algorithms to analyze transaction data and identify patterns of fraudulent activity. The system learns from historical data and uses that knowledge to detect and prevent future fraud attempts. This approach has been highly effective, with VAA reportedly blocking $25 billion in fraudulent transactions in 2020 alone.

Visa is also using AI to enhance its risk assessment capabilities. The company's Risk Manager platform uses ML algorithms to analyze transaction data and identify potential fraud risks. The system can detect unusual behavior patterns, such as a sudden increase in transaction volume or an unexpected change in location, and flag them for further investigation. This allows Visa to proactively address potential risks before they turn into full-fledged cyberattacks.

Using AI for Threat Intelligence with CyberSource Threat Intelligence

Another area where Visa is using AI to counter cyberattacks is in threat intelligence. The company's CyberSource Threat Intelligence service uses ML algorithms to analyze global threat data and identify potential security threats. This information is then shared with Visa's clients, helping them stay ahead of emerging threats and minimize their risk of a cyberattack.

Real-Time Detection and Disruption of Cyberattacks with Visa Payment Fraud Disruption (PFD) Platform

Visa has also developed a tool called the Visa Payment Fraud Disruption (PFD) platform, which uses AI to detect and disrupt cyberattacks targeting Visa clients. The PFD platform analyzes transaction data in real time and identifies any unusual activity that could indicate a cyberattack. The system then alerts Visa's cybersecurity team, who can take immediate action to prevent the attack from causing harm.

In addition to these measures, Visa is also investing in the development of AI and ML technologies to further enhance its cybersecurity capabilities. The company has partnered with leading AI firms and academic institutions to develop new tools and techniques to detect and prevent cyberattacks more effectively.

Overall, Visa's use of AI and ML in its cybersecurity systems has proven highly effective in countering cyberattacks. By leveraging these technologies, Visa is able to detect and respond to threats in real time, identify patterns in data that humans may miss, and adapt to changing threat landscapes. As cyberattacks continue to evolve and become more sophisticated, Visa will likely continue to invest in AI and ML to stay ahead of the curve and protect its customers' sensitive financial information.

Meta Announces a New AI-powered Large Language Model

On Friday, Meta introduced its new AI-powered large language model (LLM) named LLaMA-13B that, in spite of being "10x smaller," can outperform OpenAI's GPT-3 model. Language assistants in the ChatGPT style could be run locally on devices like computers and smartphones, thanks to smaller AI models. It is a part of the brand-new group of language models known as "Large Language Model Meta AI," or LLAMA. 

The size of the language models in the LLaMA collection ranges from 7 billion to 65 billion parameters. In contrast, the GPT-3 model from OpenAI, which served as the basis for ChatGPT, has 175 billion parameters. 

Meta can potentially release its LLaMA model and its weights available as open source, since it has trained models through the openly available datasets like Common Crawl, Wkipedia, and C4. Thus, marking a breakthrough in a field where Big Tech competitors in the AI race have traditionally kept their most potent AI technology to themselves.   

In regards to the same, Project member Guillaume’s tweet read "Unlike Chinchilla, PaLM, or GPT-3, we only use datasets publicly available, making our work compatible with open-sourcing and reproducible, while most existing models rely on data which is either not publicly available or undocumented." 

Meta refers to its LLaMA models as "foundational models," which indicates that the company intends for the models to serve as the basis for future, more sophisticated AI models built off the technology, the same way OpenAI constructed ChatGPT on the base of GPT-3. The company anticipates using LLaMA to further applications like "question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of present language models" and to aid in natural language research. 

While the top-of-the-line LLaMA model (LLaMA-65B, with 65 billion parameters) competes head-to-head with comparable products from rival AI labs DeepMind, Google, and OpenAI, arguably the most intriguing development comes from the LLaMA-13B model, which, as previously mentioned, can reportedly outperform GPT-3 while running on a single GPU when measured across eight common "common sense reasoning" benchmarks like BoolQ, PIQA LLaMA-13B opens the door for ChatGPT-like performance on consumer-level hardware in the near future, unlike the data center requirements for GPT-3 derivatives. 

In AI, parameter size is significant. A parameter is a variable that a machine-learning model employs in order to generate hypotheses or categorize data as input. The size of a language model's parameter set significantly affects how well it performs, with larger models typically able to handle more challenging tasks and generate output that is more coherent. However, more parameters take up more room and use more computing resources to function. A model is significantly more efficient if it can provide the same outcomes as another model with fewer parameters. 

"I'm now thinking that we will be running language models with a sizable portion of the capabilities of ChatGPT on our own (top of the range) mobile phones and laptops within a year or two," according to Simon Willison, an independent AI researcher in an Mastodon thread analyzing and monitoring the impact of Meta’s new AI models. 

Currently, a simplified version of LLaMA is being made available on GitHub. The whole code and weights (the "learned" training data in a neural network) can be obtained by filling out a form provided by Meta. A wider release of the model and weights has not yet been announced by Meta.  

Researchers Develop AI Cyber Defender to Tackle Cyber Actors

A recently developed deep reinforcement learning (DRL)-based artificial intelligence (AI) system can respond to attackers in a simulated environment and stop 95% of cyberattacks before they get more serious. 

The aforementioned findings were made by researchers from the Department of Energy’s Pacific Northwest National Laboratory based on an abstract simulation of the digital conflict between threat actors and defenders in a network and trained four different DRL neural networks in order to expand rewards based on minimizing compromises and network disruption. 

The simulated attackers transitions from the initial access and reconnaissance phase to other attack stages until they arrived at their objective, i.e. the impact and exfiltration phase. Apparently, these strategies were based on the classification of the MITRE ATT&CK architecture. 

Samrat Chatterjee, a data scientist who presented the team's work at the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, DC, on February 14, claims that the successful installation and training of the AI system on the simplified attack surfaces illustrates the defensive responses to cyberattacks that, in current times, could be conducted by an AI model. 

"You don't want to move into more complex architectures if you cannot even show the promise of these techniques[…]We wanted to first demonstrate that we can actually train a DRL successfully and show some good testing outcomes before moving forward," says Chatterjee. 

AI Emerging as a New Trend in Cybersecurity 

Machine learning (ML) and AI tactics have emerged as innovative trends to administer cybersecurity in a variety of fields. This development in cybersecurity has started from the early integration of ML in email security in the early 2010s to utilizing ChatGPT and numerous AI bots that we see today to analyze code or conduct forensic analysis. The majority of security products now incorporate a few features that are powered by machine learning algorithms that have been trained on massive datasets. 

Yet, developing an AI system that is capable of proactive protection is still more of an ideal than a realistic approach. The PNNL research suggests that an AI defender could be made possible in the future, despite the many obstacles that still need to be addressed by researchers. 

"Evaluating multiple DRL algorithms trained under diverse adversarial settings is an important step toward practical autonomous cyber defense solutions[…] Our experiments suggest that model-free DRL algorithms can be effectively trained under multistage attack profiles with different skill and persistence levels, yielding favorable defense outcomes in contested settings," according to a statement published by the PNNL researchers. 

How the System Uses MITRE ATT&CK 

The initial objective of the research team was to develop a custom simulation environment based on an open-source toolkit, Open AI Gym. Through this environment, the researchers created attacker entities with a range of skill and persistence levels that could employ a selection of seven tactics and fifteen techniques from the MITRE ATT&CK framework. 

The attacker agents' objectives are to go through the seven attack chain steps—from initial access to execution, from persistence to command and control, and from collection to impact—in the order listed. 

According to Chatterjee of PNNL, it can be challenging for the attacker to modify their strategies in response to the environment's current state and the defender's existing behavior. 

"The adversary has to navigate their way from an initial recon state all the way to some exfiltration or impact state[…] We're not trying to create a kind of model to stop an adversary before they get inside the environment — we assume that the system is already compromised," says Chatterjee. 

Not Ready for Prime Time 

In the experiments, it was revealed that a particular reinforcement learning technique called a Deep Q Network successfully solved the defensive problem by catching 97% of the intruders in the test data set. Yet the research is just the beginning. Yet, security professionals should not look for an AI assistant to assist them with incident response and forensics anytime soon.  

One of the many issues that are required to be resolved is getting RL and deep neural networks to explain the causes that affected their decision, an area of research called explainable reinforcement learning (XRL).  

Moreover, the rapid emergence of AI technology and finding the most effective tactics to train the neutral network are both a challenge that needs to be addressed, according to Chatterjee.  

Zero-Knowledge Encryption Might Protect User Rights


Web3 is an evaluation of the internet that moves past a centralized structure and tries to connect data in a decentralized way in order to offer a speedy and individualized user experience. This version of the internet is sometimes referred to as the third generation of the web.Web3 sometimes referred to as the Semantic Web, is based on AI and ML and employs blockchain technology to protect the security and privacy of user data.

Role of Zero-Knowledge Encryption

Using specific user keys, zero-knowledge encryption protects data. No one other than the user may access their encrypted files because administrators and developers do not know or have access to them. 

Zero-knowledge proofs, which may verify the truth of a proposition without revealing the underlying data, make this possible. Zero-knowledge cryptography enables information to be "private and useable at the same time," according to Aleo's CEO Alex Pruden, in contrast to other well-known types of encryption such as end-to-end models used in private messaging apps, through which only users and senders may read information. Without disclosing personal information about yourself, you can demonstrate your trustworthiness with zero-knowledge proof.

Decentralized identity (DCI) constructions, tokenization, and self-hosted wallets are three features of Web3 that promote user ownership of data and algorithms. Zero-knowledge proofs and least privilege are two techniques used in decentralized computing (DCI).

Reasons for  Zero-Knowledge Encryption

One drawback of zero-knowledge encryption is that it frequently leaves users unable to access their data moving forward if they ever need to find their encryption key or password. Because it requires more work to securely transfer and store user data, service providers that offer the full zero-knowledge encryption guarantee are often slower than their less secure competitors.

There is no better alternative than zero-knowledge encryption if a user wishes to maintain the privacy and security of their data while still hosting it on an external server for simple data management.

Using ChatGPT by Employees Poses Some Risks


As of November, when ChatGPT became available for general use, employers have been asking questions regarding its use cases for more than two months. As part of this process, it is necessary to determine how the tool should be integrated into workplace policies and how compliance can be maintained.  

Aspects of competence 

Using artificial intelligence, ChatGPT is a language platform trained to respond automatically to human speech and interact with the user. The process by which AI is trained, such as ChatGPT. It is based on feeding large data sets to computer algorithms. Once a model has been developed, it is evaluated to see how well it can make predictions based on data that has not yet been observed.  

In the next step, an AI tool will be tested to determine if it can cope with large amounts of new data that it has never been exposed to before it is turned into an in-house tool. 

While Chat GPT can improve the efficiency of workplace processes and improve worker productivity as well, it also poses legal risks to employers who choose to use it. When employees use ChatGPT to perform their job duties, certain issues can potentially arise for employers as a result of AI's ability to learn and train. When employees use a source like ChatGPT to receive information related to their work, they may be concerned about the accuracy and bias of the information they are receiving. 

The Use of Artificial Intelligence and its Accuracy 

ChatGPT's capability to create an AI language model based on information is only able to be as good as what information it acquires during its training process. As much as ChatGPT is trained to apply vast swaths of online information to a range of tasks, there are still gaps in its knowledge base. 

As of the current version of ChatGPT, the only training available for the data sets through 2021 is for data sets. Also, the tool pulls data from the Internet, and the data can be inaccurate at times, so no guarantee can be made. As long as employees do not fact-check information they rely on in connection with their job and do not rely on ChatGPT for work-related information, then there can be problems and risks if employees do not watch out for where they send that information and how they use it. 

To ensure that employees are protected from the misuse of the information in ChatGPT in the context of their work, employers should set policies that detail how employees must handle the information they receive. 

The Existence of an Inherent Bias 

It is also pertinent to note that AI is subject to inherent biases by nature. As it relates to the employment discrimination laws that the Equal Employment Opportunity Commission enforces, the Equal Employment Opportunity Commission is focused on this issue. Further, state and local legislators are proposing legislation that restricts the use of AI software by employers, and some of these laws have already been passed. 

There is a direct link between what artificial intelligence (AI) says and what information it receives from the people who determine what types of information the AI receives and what the AI will provide. As a result, ChatGPT could demonstrate some of this bias when it responds to questions presented through "conversation" with it by providing different types of responses. 

There is also the possibility that certain decisions made by ChatGPT may be construed as discriminatory if ChatGPT is consulted regarding employment decisions. There are also potential compliance issues if AI is used in employment decisions based on state and local laws in some states and municipalities. These laws require notice of the use of AI and/or audits when it is used. As a prerequisite for its use in certain employment contexts, this should be done first. 

Employers should include a prohibition on the use of artificial intelligence in connection with making employment decisions. This is because AI has the risk of causing bias in the employment process. The legal department does not have the authority to approve this action. 

Respecting Privacy and Confidentiality

Employers have to consider privacy concerns as well when it comes to using ChatGPT in the context of their work to be able to guarantee employee confidentiality. Employees should be aware that there is a potential that they will be sharing proprietary, confidential, or trade secret information when they interact with ChatGPT through "conversations." 

Even though ChatGPT claims it does not keep any information you give it during a conversation, it does process the conversation and incorporate what it's learned. As well, it is no secret that users send information to ChatGPT over the internet. Therefore, there can be no guarantee that any information sent over the internet will be secure. 

In the case that an employee discloses confidential employer information to ChatGPT, that information could be impacted. As part of employee confidentiality policies and agreements, employers should make sure that employees are prohibited from referring to or entering confidential, proprietary, or trade secret information into artificial intelligence chatbots or language models, such as ChatGPT, embedded in their AI. 

The theory that you would not necessarily be disclosing a trade secret if you gave the necessary information to an online chatbot can be made persuasive. However, having been trained on a wide swath of online information, ChatGPT may provide employees with information through the tool that is trademarked, copyrighted, or a group's intellectual property. This may expose employers to legal risks as they may receive and use information that is protected by third-party rights. 

Concerns of Employers, in General  

The employer should also take into consideration how much time and resources they are willing to devote to allowing employees to use ChatGPT in the course of their work. This is in addition to legal concerns.  

The use of ChatGPT by employers in their workplaces has reached a crossroads where employers are choosing whether to allow or restrict the use of this technology. As an employer, you should weigh the potential cost and efficiency of implementing ChatGPT as an alternative to employees performing such tasks as creating simple reports, writing routine letters and emails, and creating presentations, for instance, against the possibility of losing opportunities for employees to learn and grow by doing those things themselves. 

A redesigned and improved version of ChatGPT is scheduled to be released within the year, so ChatGPT will not be going away on its own. Employers will ultimately need to consider how to implement it. This is because a completely updated version upcoming shortly will be even better. 

While ChatGPT presents employers with several risks, it can also be used to maximize its benefits. As of right now, the discussion has just begun. As with ChatGPT, employers will have to learn about this and test it out for a short while before they can make use of it.

ChatGPT: A Potential Risk to Data Privacy

ChatGPT, within two months of its release, seems to have taken over the world like a storm. The consumer application has achieved 100 million active users, making it the fastest-growing product ever. Users are intrigued by the tool's sophisticated capabilities, although apprehensive about its potential to upend numerous industries. 

One of the less discussed consequences in regard to ChatGPT is its privacy risk. Google only yesterday launched Bard, its own conversational AI, and others will undoubtedly follow. Technology firms engaged in AI development have certainly entered a race. 

The issue would be its technology, which is entirely based on users’ personal data. 

300 Billion Words, How Many Are Yours? 

ChatGPT is apparently based on a massive language model, which backs up an enormous amount of data to operate and enhance its functions. Implying, the model gets more adept at seeing patterns, foreseeing what will happen next, and producing credible text as more data is used to train it. 

OpenAI, the developer of ChatGPT, sourced the Chatbot model with some 3 million words systematically taken from the internet –  via books, articles, websites, and posts – which also undeniably involves online users’ personal information, gathered without their consent. 

Every blog post, product review, or comment written on an article, which exists or ever existed in the online world has a good chance that the data or information involved it is was consumed by ChatGPT. 

What is the Issue? 

The gathered data used in order to train ChatGPT is problematic for numerous reasons. 

First, the collected data is unconsented, since none of the online users were ever asked if OpenAI could use their seemingly personal information. Thus, this would be a clear violation of privacy, especially when the data is sensitive and can be used to locate us, identify our loved ones, or identify ourselves. 

The usage of data can compromise what we refer to as contextual integrity even when the data is publicly available. This is a cornerstone idea in discussions about privacy law. Information on people must not be made public outside of the context in which it was first created. 

Moreover, OpenAI does not include any procedure for users to monitor whether the company has their personal information in-store, or to request it to be taken down. The European General Data Protection Regulation (GDPR), which guarantees this right, is still being discussed as to whether ChatGPT complies with its criteria. 

This “right to be forgotten” is specifically essential when it comes to situations involving information that is inaccurate or misleading, which seems to be a regular occurrence in ChatGPT. 

Furthermore, the scraped data that ChatGPT was trained on may be confidential or protected by copyright. For instance, the tool replicated the opening few chapters of Joseph Heller's copyrighted book Catch-22. 

Finally, OpenAI did not pay for the internet data it downloaded. Its creators—individuals, website owners, and businesses—were not being compensated. This is especially remarkable in light of the recent US$29 billion valuation of OpenAI, which is more than double its value in 2021. 

OpenAI has also recently announced ChatGPT Plus, which is a paid subscription plan that will provide users ongoing access to the tool, swift response times, and priority access to its new feature. By 2024, it is anticipated that this approach would help generate $1 billion in revenue. 

None of this would have been possible without the usage of ‘our’ data, acquired and utilized without our consent. 

Time to Consider the Issue? 

According to some professionals and experts, ChatGPT is a “tipping point for AI” - The realisation of technological advancement that can revolutionize the way we work, learn, write, and even think. 

Despite its potential advantages, we must keep in mind that OpenAI is a private, for-profit organization whose objectives and business demands may not always coincide with those of the larger community requirements. 

The privacy hazards associated with ChatGPT should serve as a caution. And as users of an increasing number of AI technologies, we need to exercise extreme caution when deciding what data to provide such tools with.  

Microsoft Announces New OpenAI-Powered Bing

Microsoft has recently launched the newest version of its search engine Bing, which includes an upgraded version of the same AI technology that powers chatbot ChatGPT. 

The organization announces the product launch alongside the new AI-enhanced features for its Edge browser, promising users that the two will offer a fresh experience for acquiring information online. 

Microsoft, in a blog post, claims the new version as a technical breakthrough with its next-generation OpenAI model. “We’re excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 – and it is even faster, more accurate, and more capable,” the blog post states.  

In regards to the product launch, Microsoft CEO Satya Nadella says “race starts today, and we’re going to move and move fast […] “Most importantly, we want to have a lot of fun innovating again in search, because it’s high time.” at a special event at Microsoft headquarters in Redmond, Washington. 

According to Nadella, he believed it was ready to transform how people interact with other applications and do online searches. "This technology will reshape pretty much every software category that we know," he said. 

With the latest advancements, Bing will now respond to search queries in a more detailed manner, rather than just links and websites. 

Additionally, Bing users can now interact with bots to efficiently customize their queries. On the right side of a search results page, more contextual responses will be added. 

The announcement comes a day after Google unveiled information regarding Bard, its own brand-new chatbot. 

With both companies striving to launch their products to the market, Microsoft's investment, according to analyst Dan Ives of Wedbush Securities, will "massively increase" the company's capacity to compete, he said in a note to investors following the news. 

"This is just the first step on the AI front ... as [the] AI arms race takes place among Big Tech," he added. Microsoft has been spending billions on artificial intelligence and was an early supporter of San Francisco-based OpenAI. 

It declared last month that it will be extending its partnership with OpenAI through a "multiyear, multibillion-dollar investment." 

Bing will employ OpenAI technology, according to Microsoft, which is even more sophisticated than the ChatGPT technology announced last year. Additionally, the powers will be added to its Edge web browser.   

AI Models Produces Photos of Real People and Copyrighted Images

The infamous image generation models are used in order to produce identifiable photos of actual people. This leads to the privacy infringement of numerous individuals, as per a new research. 

The study demonstrates how these AI systems can be programmed to reproduce precisely copyrighted artwork and medical images. It is a result that might help artists who are suing AI companies for copyright violations.  

Research: Extracting Training Data from Diffusion Models 

Researchers from Google, DeepMind, UC Berkeley, ETH Zürich, and Princeton obtained their findings by repeatedly prompting Google’s Imagen with image captions, like the user’s name. Following this, they analyzed if any of the images they produced matched the original photos stored in the model's database. The team was successful in extracting more than 100 copies of photos from the AI's training set. 

These image-generating AI models are apparently produced over vast data sets, that consist of images with captions that have been taken from the internet. The most recent technology works by taking images in the data sets and altering pixels individually until the original image is nothing more than a jumble of random pixels. The AI model then reverses the procedure to create a new image from the pixelated mess. 

According to Ryan Webster, a Ph.D. student from the University of Caen Normandy, who has studied privacy in other image generation models but is not involved in the research, the study is the first to demonstrate that these AI models remember photos from their training sets. This could also serve as an implication for startups wanting to use AI models in health care since it indicates that these systems risk leaking users’ private and sensitive data. 

Eric Wallace, a Ph.D. scholar who was involved in the study group, raises concerns over the privacy issue and says they hope to raise alarm regarding the potential privacy concerns with these AI models before they are extensively implemented in delicate industries like medicine. 

“A lot of people are tempted to try to apply these types of generative approaches to sensitive data, and our work is definitely a cautionary tale that that’s probably a bad idea unless there’s some kind of extreme safeguards taken to prevent [privacy infringements],” Wallace says. 

Another major conflict between AI businesses and artists is caused by the extent to which these AI models memorize and regurgitate photos from their databases. Two lawsuits have been filed against AI by Getty Images and a group of artists who claim the company illicitly scraped and processed their copyrighted content. 

The researchers' findings will ultimately aid artists to claim that AI companies have violated their copyright. The companies may have to pay artists whose work was used to train Stable Diffusion if they can demonstrate that the model stole their work without their consent. 

According to Sameer Singh, an associate professor of computer science at the University of California, Irvine, these findings hold paramount importance. “It is important for general public awareness and to initiate discussions around the security and privacy of these large models,” he adds.  

ChatGPT: When Cybercrime Meets the Emerging Technologies

The immense capability of ChatGPT has left the entire globe abuzz. Indeed, it solves both practical and abstract problems, writes and debugs code, and even has the potential to aid with Alzheimer's disease screening. The OpenAI AI-powered chatbot, however, is at high risk of abuse, as is the case with many new technologies. 

How Can ChatGPT be Used Maliciously? 

Recently, researchers from Check Point Software discovered that ChatGPT could be utilized to create phishing emails. When combined with Codex, a natural language-to-code system by OpenAI, ChatGPT can develop and disseminate malicious code. 

According to Sergey Shykevich, threat intelligence group manager at Check Point Software, “Our researchers built a full malware infection chain starting from a phishing email to an Excel document that has malicious VBA [Visual Basic for Application] code. We can compile the whole malware to an executable file and run it in a machine.” 

He adds that ChatGPT primarily produces “much better and more convincing phishing and impersonation emails than real phishing emails we see in the wild now.” 

In regards to the same, Lorrie Faith Cranor, director and Bosch Distinguished Professor of the CyLab Security and Privacy Institute and FORE Systems Professor of computer science and of engineering and public policy at Carnegie Mellon University says, “I haven’t tried using ChatGPT to generate code, but I’ve seen some examples from others who have. It generates code that is not all that sophisticated, but some of it is actually runnable code[…]There are other AI tools out there for generating code, and they are all getting better every day. ChatGPT is probably better right now at generating text for humans, and may be particularly well suited for generating things like realistic spoofed emails.” 

Moreover, the researchers have also discovered hackers that create malicious tools like info-stealers and dark web markets using ChatGPT. 

What AI Tools are More Worrisome? 

Cranor says “I think to use these [AI] tools successfully today requires some technical knowledge, but I expect over time it will become easier to take the output from these tools and launch an attack[…]So while it is not clear that what the tools can do today is much more worrisome than human-developed tools that are widely distributed online, it won’t be long before these tools are developing more sophisticated attacks, with the ability to quickly generate large numbers of variants.” 

Furthermore, complications could as well arise from the inability to detect whether the code was created by utilizing ChatGPT. “There is no good way to pinpoint that a specific software, malware, or even phishing email was written by ChatGPT because there is no signature,” says Shykevich. 

What Could be the Solution? 

One of the methods OpenAI is opting for is to “watermark” the output of GPT models, which could later be used to determine whether they are created by AI or humans. 

In order to safeguard companies and individuals from these AI-generated threats, Shykevich advises using appropriate cybersecurity measures. While the current safeguards are still in effect, it is critical to keep upgrading and bolstering their application. 

“Researchers are also working on ways to use AI to discover code vulnerabilities and detect attacks[…]Hopefully, advances on the defensive side will be able to keep up with advances on the attacker side, but that remains to be seen,” says Cranor. 

While ChatGPT and other AI-backed systems have the potential to fundamentally alter how individuals interact with technology, they also carry some risk, particularly when used in dangerous ways. 

“ChatGPT is a great technology and has the potential to democratize AI,” adds Shykevich. “AI was kind of a buzzy feature that only computer science or algorithmic specialists understood. Now, people who aren’t tech-savvy are starting to understand what AI is and trying to adopt it in their day-to-day. But the biggest question, is how would you use it—and for what purposes?”  

ChatGPT's Effective Corporate Usage Might Eliminate Systemic Challenges


Today's AI is highly developed. Artificial intelligence combines disciplines that make an effort to essentially duplicate the capacity of the human brain to learn from experience and generate judgments based on that experience. Researchers utilize a variety of tactics to do this. In one paradigm, brute force is used, where the computer system cycles through all possible solutions to a problem until it finds the one that has been proven to be right.

"ChatGPT is really restricted, but good enough at some things to provide a misleading image of brilliance. It's a mistake to be depending on it for anything essential right now," said OpenAI CEO Sam Altman when the software was first launched on November 30. 

According to Nicola Morini Bianzino, global chief technology officer at EY, there's presently no killer use case for ChatGPT in the industry which will significantly affect both the top and bottom lines. They projected that there will be an explosion of experimentation over the next six to twelve months, particularly after businesses are able to develop over the top of ChatGPT utilizing OpenAI's API.

While OpenAI CEO Sam Altman has acknowledged that ChatGPT and other generative AI technologies face several challenges, ranging from possible ethical implications to accuracy problems.

According to Bianzino, this possibility for generative AI's future will have a big impact on enterprise software since companies would have to start considering novel ways to organize data inside an enterprise that surpasses conventional analytics tools. The ways people access and use information inside the company will alter as ChatGPT and comparable tools advance and become more capable of being trained on an enterprise's data in a secure manner.

As per Bianzino, the creation of text and documentation will also require training and alignment to the appropriate ontology of the particular organization, as well as containment, storage, and control inside the enterprise. He stated that business executives, including the CTO and CIO, must be aware of these trends because, unlike quantum computing, which may not even be realized for another 10 to 15 years, the actual potential of generative AI may be realized within the next six to twelve months.

Decentralized peer-to-peer technology mixed with blockchain and smart contracts capabilities overcome the traditional challenges of privacy, traceability, trust, and security. By doing this, data owners can share insights from data without having to relocate or otherwise give up ownership of it.

Is AI Transforming the Cybersecurity Sector? 

Artificial intelligence and machine learning (AI/ML) systems have proven to be effective in improving the sophistication of phishing lures, creating fake profiles, and developing basic malware. Security experts have demonstrated that a complete attack chain may be established, and malicious hackers have already begun experimenting with AI-generated code.

The Check Point Research team employed current AI tools to design a whole attack campaign which began with a phishing email sent by OpenAI's ChatGPT that prompts the target to open an Excel document. Researchers also developed an Excel macro that runs malware obtained from a URL and a Python script to infect the intended system using the Codex AI programming tool.

To evaluate the effectiveness of AI in data collection and team response to cyberattacks on vital systems and services, as well as to draw attention to the need for solutions that enhance human-machine collaboration to lower cyber risk. 

In recent weeks, ChatGPT, a large language model (LLM) based on OpenAI's generative pre-trained transformer (GPT-3) third iteration, sparked a scope of what-if scenarios for the possible uses of AI/ML. Due to the dual-use nature of AI/ML models, firms are looking for ways to use the technology to increase efficiency, while campaigners for digital rights are concerned about the effects the technology will have on businesses and employees.   

However, other aspects of security and privacy are also being impacted by AI/ML. To enhance profiles used for fraud and misinformation, generative neural networks (GNNs) were utilized to produce photographs of fake persons that look real but do not portray a real person. 

The employment of the most advanced artificial intelligence system by cyber attackers does not, as of yet, make the attacks more difficult to spot. However, by emphasizing the technical signs, cybersecurity tools can still detect the issue. Even the most effective fake imitation would be defeated by the procedures used to double-check requests to modify an account for payment and paycheck transfer unless the threat organization had access to or control over the further layers of security that have become increasingly frequent.