In recent years, large language models have made significant strides in their conversational and research capabilities, providing vast knowledge on almost any topic. However, this progress has raised concerns about the security of these technologies. Jailbreaking, as it is called, is a method of exploiting large AI knowledge unrestrictedly. It is achieved by prompting specific commands that trigger the AI to answer without regard to its built-in security measures. Jailbreak prompts can range from straightforward commands to more abstract narratives designed to coax the chatbot into bypassing its constraints. The overall goal is to find specific language that convinces the AI to unleash its full, uncensored potential.
jailbroken chatgbt
As AI systems like ChatGPT continue to advance, there is growing concern that techniques to bypass their safety features may become more prevalent. However, a focus on responsible innovation and enhancing safeguards could help mitigate potential risks.
Organizations like OpenAI are already taking proactive measures to enhance the security of their chatbots. They conduct red team exercises to identify vulnerabilities, enforce access controls, and diligently monitor for malicious activity. The OWASP Top 10 for Large Language Model Applications project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing Large Language Models (LLMs). The project provides a list of the top 10 most critical vulnerabilities often seen in LLM applications, highlighting their potential impact, ease of exploitation, and prevalence in real-world applications. Examples of vulnerabilities include prompt injections, data leakage, inadequate sandboxing, and unauthorized code execution, among others. The goal is to raise awareness of these vulnerabilities, suggest remediation strategies, and ultimately improve the security posture of LLM applications .
In conclusion, while the potential risks of large language models are real, responsible innovation and enhanced safeguards can help mitigate these risks. Organizations like OpenAI and OWASP are already taking proactive measures to enhance the security of their chatbots, and it is essential that others follow suit to ensure the safe and responsible deployment of these powerful technologies.
Cryptocurrency payments startup Ramp Network, based in Poland and London, has recently secured an impressive $70 million in fresh investment, defying the challenging fundraising landscape and the turmoil in the crypto industry. This Series B funding round, co-led by Mubadala Capital from the UAE and Korelya Capital from France, follows their successful $53 million Series A round less than a year ago. It stands as one of the largest funding rounds in the history of the Polish startup ecosystem.
What is Ramp Network?
Ramp Network provides a solution that allows clients, such as crypto wallets, exchanges, NFT marketplaces, and gaming companies, to seamlessly integrate crypto payment infrastructure into their existing systems. This integration enables users to purchase crypto assets without the need to switch between multiple applications. Ramp Network supports major payment methods like debit and credit cards, bank transfers, Apple Pay, and Google Pay, making it accessible to users in over 150 countries.
Navigating a Bearish Market
At a time when the global crypto market experienced a significant downturn, with a two-thirds decrease in value due to inflation and a bleak economic outlook, Ramp Network has managed to thrive. In 2022 alone, the platform witnessed a staggering 240% increase in transaction volumes compared to the previous year. Additionally, the total number of unique users has surged by over 600%. This remarkable growth sets Ramp Network apart from its competitors and demonstrates its resilience in the face of challenging market conditions.
Strategic Investments
Ramp Network’s Series B funding round attracted prominent investors from across the globe. Mubadala Capital, an Emirati venture capitalist, and Korelya Capital, a French venture capitalist, led the round. Furthermore, existing investor Balderton Capital, a UK-based venture capitalist, and Polish VC Cogito Capital also participated in the funding. The investments not only validate the potential of Ramp Network but also provide the necessary capital to further develop and expand its innovative crypto payment infrastructure.
The Path Ahead for Ramp Network
Szymon Sypniewicz, co-founder and CEO of Ramp Network, envisions making the platform even more accessible to a wider audience through the Series B funding. He plans to enter local markets and enable local payment methods, particularly focusing on Latin America and Asia. These regions have witnessed explosive crypto adoption and represent Ramp Network’s next strategic targets. The company also intends to continue hiring and invest in the further enhancement of its product line.
“We’re fully committed to our vision of making Web3 a reality for everyone. We recognize that a bear market is a builder’s market, and we’re in it for the long haul,” affirms Sypniewicz.
The Significance of Ramp Network in the Crypto World
Ramp Network’s ability to thrive during a challenging economic downturn highlights its pivotal role in the cryptocurrency ecosystem. Often compared to Stripe, a market-leading tool for e-commerce, Ramp Network aims to benefit from the growing trend of traditional companies embracing Web3. Web3 refers to the next generation of blockchain-powered businesses that prioritize decentralization and democratize ownership, in contrast to centralized platforms like Facebook and YouTube. This shift presents a significant opportunity, and Ramp Network is poised to capitalize on it, unlocking the industry’s vast potential.
Conclusion
Ramp Network’s recent funding success solidifies its position as a frontrunner in the crypto payments space. With its innovative infrastructure, the platform has enabled seamless integration of crypto payments for various businesses. Despite the bearish market conditions and the challenges faced by the crypto industry, Ramp Network has demonstrated remarkable growth and resilience. As it expands into local markets and continues to enhance its product line, Ramp Network is well-positioned to drive the adoption of cryptocurrencies and contribute to the realization of a decentralized Web3 future.
Cloud security is a collection of procedures and technology designed to address external and internal threats to business security. Organizations need cloud security as they move toward their digital transformation strategy and incorporate cloud-based tools and services as part of their infrastructure.
The terms digital transformation and cloud migration have been used regularly in enterprise settings over recent years. While both phrases can mean different things to different organizations, each is driven by a common denominator: the need for change.
As enterprises embrace these concepts and move toward optimizing their operational approach, new challenges arise when balancing productivity levels and security. While more modern technologies help organizations advance capabilities outside the confines of on-premise infrastructure, transitioning primarily to cloud-based environments can have several implications if not done securely.
Striking the right balance requires an understanding of how modern-day enterprises can benefit from the use of interconnected cloud technologies while deploying the best cloud security practices.
What is cloud computing?
The “cloud” or, more specifically, “cloud computing” refers to the process of accessing resources, software, and databases over the Internet and outside the confines of local hardware restrictions. This technology gives organizations flexibility when scaling their operations by offloading a portion, or majority, of their infrastructure management to third-party hosting providers.
The most common and widely adopted cloud computing services are:
IaaS (Infrastructure-as-a-Service): A hybrid approach, where organizations can manage some of their data and applications on-premise while relying on cloud providers to manage servers, hardware, networking, virtualization, and storage needs.
PaaS (Platform-as-a-Service): Gives organizations the ability to streamline their application development and delivery by providing a custom application framework that automatically manages operating systems, software updates, storage, and supporting infrastructure in the cloud.
SaaS (Software-as-a-Service): Cloud-based software hosted online and typically available on a subscription basis. Third-party providers manage all potential technical issues, such as data, middleware, servers, and storage, minimizing IT resource expenditures and streamlining maintenance and support functions.
Why is cloud security important?
In modern-day enterprises, there has been a growing transition to cloud-based environments and IaaS, Paas, or SaaS computing models. The dynamic nature of infrastructure management, especially in scaling applications and services, can bring a number of challenges to enterprises when adequately resourcing their departments. These as-a-service models give organizations the ability to offload many of the time-consuming, IT-related tasks.
As companies continue to migrate to the cloud, understanding the security requirements for keeping data safe has become critical. While third-party cloud computing providers may take on the management of this infrastructure, the responsibility of data asset security and accountability doesn’t necessarily shift along with it.
By default, most cloud providers follow best security practices and take active steps to protect the integrity of their servers. However, organizations need to make their own considerations when protecting data, applications, and workloads running on the cloud.
Security threats have become more advanced as the digital landscape continues to evolve. These threats explicitly target cloud computing providers due to an organization’s overall lack of visibility in data access and movement. Without taking active steps to improve their cloud security, organizations can face significant governance and compliance risks when managing client information, regardless of where it is stored.
Cloud security should be an important topic of discussion regardless of the size of your enterprise. Cloud infrastructure supports nearly all aspects of modern computing in all industries and across multiple verticals.
However, successful cloud adoption is dependent on putting in place adequate countermeasures to defend against modern-day cyberattacks. Regardless of whether your organization operates in a public, private, or hybrid cloud environment, cloud security solutions and best practices are a necessity when ensuring business continuity.
What are some cloud security challenges?
Lack of visibility It’s easy to lose track of how your data is being accessed and by whom, since many cloud services are accessed outside of corporate networks and through third parties.
Multitenancy Public cloud environments house multiple client infrastructures under the same umbrella, so it’s possible your hosted services can get compromised by malicious attackers as collateral damage when targeting other businesses.
Access management and shadow IT While enterprises may be able to successfully manage and restrict access points across on-premises systems, administering these same levels of restrictions can be challenging in cloud environments. This can be dangerous for organizations that don’t deploy bring-your-own device (BYOD) policies and allow unfiltered access to cloud services from any device or geolocation.
Compliance Regulatory compliance management is oftentimes a source of confusion for enterprises using public or hybrid cloud deployments. Overall accountability for data privacy and security still rests with the enterprise, and heavy reliance on third-party solutions to manage this component can lead to costly compliance issues.
Misconfigurations Misconfigured assets accounted for 86% of breached records in 2019, making the inadvertent insider a key issue for cloud computing environments. Misconfigurations can include leaving default administrative passwords in place, or not creating appropriate privacy settings.
What types of cloud security solutions are available?
Identity and access management (IAM) Identity and access management (IAM) tools and services allow enterprises to deploy policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services. The core functionality of IAM is to create digital identities for all users so they can be actively monitored and restricted when necessary during all data interactions
Data loss prevention (DLP) Data loss prevention (DLP) services offer a set of tools and services designed to ensure the security of regulated cloud data. DLP solutions use a combination of remediation alerts, data encryption, and other preventative measures to protect all stored data, whether at rest or in motion.
Security information and event management (SIEM) Security information and event management (SIEM) provides a comprehensive security orchestration solution that automates threat monitoring, detection, and response in cloud-based environments. Using artificial intelligence (AI)-driven technologies to correlate log data across multiple platforms and digital assets, SIEM technology gives IT teams the ability to successfully apply their network security protocols while being able to quickly react to any potential threats.
Business continuity and disaster recovery Regardless of the preventative measures organizations have in place for their on-premise and cloud-based infrastructures, data breaches and disruptive outages can still occur. Enterprises must be able to quickly react to newly discovered vulnerabilities or significant system outages as soon as possible. Disaster recovery solutions are a staple in cloud security and provide organizations with the tools, services, and protocols necessary to expedite the recovery of lost data and resume normal business operations.
An overview of cloud security
Cloud security is a collection of procedures and technology designed to address external and internal threats to business security. Organizations need cloud security as they move toward their digital transformation strategy and incorporate cloud-based tools and services as part of their infrastructure.
The terms digital transformation and cloud migration have been used regularly in enterprise settings over recent years. While both phrases can mean different things to different organizations, each is driven by a common denominator: the need for change.
As enterprises embrace these concepts and move toward optimizing their operational approach, new challenges arise when balancing productivity levels and security. While more modern technologies help organizations advance capabilities outside the confines of on-premise infrastructure, transitioning primarily to cloud-based environments can have several implications if not done securely.
Striking the right balance requires an understanding of how modern-day enterprises can benefit from the use of interconnected cloud technologies while deploying the best cloud security practices. Learn more about cloud security solutions What is cloud computing?
The “cloud” or, more specifically, “cloud computing” refers to the process of accessing resources, software, and databases over the Internet and outside the confines of local hardware restrictions. This technology gives organizations flexibility when scaling their operations by offloading a portion, or majority, of their infrastructure management to third-party hosting providers.
The most common and widely adopted cloud computing services are:
IaaS (Infrastructure-as-a-Service): A hybrid approach, where organizations can manage some of their data and applications on-premise while relying on cloud providers to manage servers, hardware, networking, virtualization, and storage needs.
PaaS (Platform-as-a-Service): Gives organizations the ability to streamline their application development and delivery by providing a custom application framework that automatically manages operating systems, software updates, storage, and supporting infrastructure in the cloud.
SaaS (Software-as-a-Service): Cloud-based software hosted online and typically available on a subscription basis. Third-party providers manage all potential technical issues, such as data, middleware, servers, and storage, minimizing IT resource expenditures and streamlining maintenance and support functions.
Why is cloud security important?
In modern-day enterprises, there has been a growing transition to cloud-based environments and IaaS, Paas, or SaaS computing models. The dynamic nature of infrastructure management, especially in scaling applications and services, can bring a number of challenges to enterprises when adequately resourcing their departments. These as-a-service models give organizations the ability to offload many of the time-consuming, IT-related tasks.
As companies continue to migrate to the cloud, understanding the security requirements for keeping data safe has become critical. While third-party cloud computing providers may take on the management of this infrastructure, the responsibility of data asset security and accountability doesn’t necessarily shift along with it.
By default, most cloud providers follow best security practices and take active steps to protect the integrity of their servers. However, organizations need to make their own considerations when protecting data, applications, and workloads running on the cloud.
Security threats have become more advanced as the digital landscape continues to evolve. These threats explicitly target cloud computing providers due to an organization’s overall lack of visibility in data access and movement. Without taking active steps to improve their cloud security, organizations can face significant governance and compliance risks when managing client information, regardless of where it is stored.
Cloud security should be an important topic of discussion regardless of the size of your enterprise. Cloud infrastructure supports nearly all aspects of modern computing in all industries and across multiple verticals.
However, successful cloud adoption is dependent on putting in place adequate countermeasures to defend against modern-day cyberattacks. Regardless of whether your organization operates in a public, private, or hybrid cloud environment, cloud security solutions and best practices are a necessity when ensuring business continuity.What are some cloud security challenges?
Lack of visibility It’s easy to lose track of how your data is being accessed and by whom, since many cloud services are accessed outside of corporate networks and through third parties.
Multitenancy Public cloud environments house multiple client infrastructures under the same umbrella, so it’s possible your hosted services can get compromised by malicious attackers as collateral damage when targeting other businesses.
Access management and shadow IT While enterprises may be able to successfully manage and restrict access points across on-premises systems, administering these same levels of restrictions can be challenging in cloud environments. This can be dangerous for organizations that don’t deploy bring-your-own device (BYOD) policies and allow unfiltered access to cloud services from any device or geolocation.
Compliance Regulatory compliance management is oftentimes a source of confusion for enterprises using public or hybrid cloud deployments. Overall accountability for data privacy and security still rests with the enterprise, and heavy reliance on third-party solutions to manage this component can lead to costly compliance issues.
Misconfigurations Misconfigured assets accounted for 86% of breached records in 2019, making the inadvertent insider a key issue for cloud computing environments. Misconfigurations can include leaving default administrative passwords in place, or not creating appropriate privacy settings.
What types of cloud security solutions are available?
Identity and access management (IAM) Identity and access management (IAM) tools and services allow enterprises to deploy policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services. The core functionality of IAM is to create digital identities for all users so they can be actively monitored and restricted when necessary during all data interactions
Data loss prevention (DLP) Data loss prevention (DLP) services offer a set of tools and services designed to ensure the security of regulated cloud data. DLP solutions use a combination of remediation alerts, data encryption, and other preventative measures to protect all stored data, whether at rest or in motion.
Security information and event management (SIEM) Security information and event management (SIEM) provides a comprehensive security orchestration solution that automates threat monitoring, detection, and response in cloud-based environments. Using artificial intelligence (AI)-driven technologies to correlate log data across multiple platforms and digital assets, SIEM technology gives IT teams the ability to successfully apply their network security protocols while being able to quickly react to any potential threats.
Business continuity and disaster recovery Regardless of the preventative measures organizations have in place for their on-premise and cloud-based infrastructures, data breaches and disruptive outages can still occur. Enterprises must be able to quickly react to newly discovered vulnerabilities or significant system outages as soon as possible. Disaster recovery solutions are a staple in cloud security and provide organizations with the tools, services, and protocols necessary to expedite the recovery of lost data and resume normal business operations.
How should you approach cloud security?
The way to approach cloud security is different for every organization and can be dependent on several variables. However, the National Institute of Standards and Technology (NIST) has made a list of best practices that can be followed to establish a secure and sustainable cloud computing framework.
The NIST has created necessary steps for every organization to self-assess their security preparedness and apply adequate preventative and recovery security measures to their systems. These principles are built on the NIST’s five pillars of a cybersecurity framework: Identify, Protect, Detect, Respond, and Recover.
Another emerging technology in cloud security that supports the execution of NIST’s cybersecurity framework is cloud security posture management (CSPM). CSPM solutions are designed to address a common flaw in many cloud environments – misconfigurations.
Cloud infrastructures that remain misconfigured by enterprises or even cloud providers can lead to several vulnerabilities that significantly increase an organization’s attack surface. CSPM addresses these issues by helping to organize and deploy the core components of cloud security. These include identity and access management (IAM), regulatory compliance management, traffic monitoring, threat response, risk mitigation, and digital asset management.
Overall:
The breakdown of common cloud computing services (IaaS, PaaS, and SaaS) adds clarity, aiding understanding of modern enterprise models. Adeptly addresses challenges, including lack of visibility, multitenancy issues, access management complexities, compliance concerns, and misconfigurations, offering valuable insights for organizations.
The recommended cloud security solutions (IAM, DLP, SIEM, Business Continuity, and Disaster Recovery) provide a comprehensive approach to risk mitigation. The article’s inclusion of NIST principles and the emerging technology CSPM further enriches its content.
In summary, the article serves as a valuable resource for organizations navigating cloud security complexities. Its blend of informative content, practical solutions, and insights into emerging technologies makes it an effective guide.
The war in Ukraine is a stark reminder of the potential for technology to be used for both good and evil. On the one hand, technology is being used to help people in a variety of ways, such as providing humanitarian assistance, connecting loved ones, and documenting war crimes. On the other hand, technology is also being used to harm people, such as carrying out airstrikes, delivering supplies to Russian forces, and spreading disinformation.
How technology is harming people
Drones: Drones are being used to carry out airstrikes on civilian targets, killing and injuring innocent people. For example, in March 2022, a Russian drone strike on a maternity hospital in Mariupol killed three people, including a pregnant woman and her child.
Robots: Robots are being used to clear minefields, defuse explosives, and evacuate civilians. However, there have been reports of robots being used to carry out attacks on civilians. For example, in April 2022, a Russian robot was reported to have opened fire on a group of civilians in Bucha.
Cyberwarfare: Cyberwarfare tactics are being used to disrupt communications, steal data, and launch denial-of-service attacks. These attacks can have a devastating impact on civilians, disrupting access to essential services and causing widespread economic damage. For example, in February 2022, a Russian cyberattack on Ukraine’s banking system caused widespread outages.
How technology is helping people
Humanitarian assistance: Technology is being used to provide humanitarian assistance to people affected by the war. For example, the World Food Programme is using drones to deliver food and other essential supplies to people in besieged areas.
Connecting loved ones: Technology is helping people to connect with loved ones who have been separated by the war. For example, the Red Cross is providing free phone calls and video chats to people who have been displaced by the conflict.
Documenting war crimes: Technology is being used to document war crimes and human rights abuses. For example, the Bellingcat investigative group is using social media footage and other open-source information to identify and track Russian war criminals.
Where tech in this war is heading
The war in Ukraine is driving technological innovation, as both sides are developing new weapons and tactics. For example, Russia is developing new types of drones, including kamikaze drones that can fly into targets and explode. Ukraine is developing new ways to counter Russian drones, such as using electronic warfare systems to disable them.
What tech will look like for future wars?
The war in Ukraine is a glimpse into the future of warfare, where technology will play an increasingly important role. In future wars, we can expect to see more use of drones, robots, and cyberwarfare. We may also see the development of new technologies, such as artificial intelligence-powered weapons that can make their own decisions about who to kill.
Other related news
Social media platforms are being used to spread disinformation and propaganda on both sides of the war in Ukraine. Russian and Ukrainian government agencies, as well as independent media outlets and individuals, are using social media to share information about the war. However, some of this information is inaccurate or misleading.
For example, Russian officials have used social media to spread false claims that the Ukrainian government is committing genocide against Russian speakers in Ukraine. Ukrainian officials have also used social media to spread false claims that the Russian military is using chemical weapons in Ukraine.
Both the Russian and Ukrainian militaries are using facial recognition technology to track and identify their opponents. Facial recognition technology can be used to identify individuals in photos and videos, even if they are wearing masks or other disguises.
For example, the Russian military is using facial recognition technology to identify Ukrainian soldiers and civilians who have been captured. The Ukrainian military is using facial recognition technology to identify Russian soldiers who have committed war crimes.
Conclusion
The war in Ukraine is a reminder of the potential for technology to be used for both good and evil. It is important to be aware of how technology is being used in the war, both to harm and to help people. We must also be prepared for the future of warfare, where technology will play an increasingly important role.
I prompted Google Bard by asking it firstly about current events. The wars taking place are very prevalent issues and many articles highlight the technology used. It didn’t take long before this AI engine was able to produce information about the wars and different perspectives on the destructive technologies.
As the world’s leading Internet television network with over 160 million members in over 190 countries, our members enjoy hundreds of millions of hours of content per day, including original series, documentaries and feature films. Of course, all our all-time favourites are right on our hands, and that is where machine learning has taken it’s berth on the podium. This is where we will dive into Machine Learning.
MONEY HEIST(2017)
Machine learning impacts many exciting areas throughout our company. Historically, personalization has been the most well-known area, where machine learning powers our recommendation algorithms. We’re also using machine learning to help shape our catalogue of movies and TV shows by learning characteristics that make content successful. Machine Learning also enables us by giving the freedom to optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network.
I believe that using machine learning as a whole can open up a lot of perspectives in our lives, where we need to push forward the state-of-the-art. This means coming up with new ideas and testing them out, be it new models and algorithms or improvements to existing ones.
Operating a large-scale recommendation system is a complex undertaking: it requires high availability and throughput, involves many services and teams, and the environment of the recommender system changes every second. In this we will introduce RecSysOps a set of best practices and lessons that we learned while operating large-scale recommendation systems at Netflix. These practices helped us to keep our system healthy:
1) reducing our firefighting time, 2) focusing on innovations and 3) building trust with our stakeholders.
RecSysOps has four key components: issue detection, issue prediction, issue diagnosis and issue resolution.
Within the four components of RecSysOps, issue detection is the most critical one because it triggers the rest of steps. Lacking a good issue detection setup is like driving a car with your eyes closed.
ALL YOUR FAVOURITE MOVIES AND TV SHOWS RIGHT HERE!
The very first step is to incorporate all the known best practices from related disciplines, as creating recommendation systems includes procedures like software engineering and machine learning, this includes all DevOps and MLOps practices such as unit testing, integration testing, continuous integration, checks on data volume and checks on model metrics.
The second step is to monitor the system end-to-end from your perspective. In a large-scale recommendation system there are many teams that often are involved and from the perspective of an ML team we have both upstream teams (who provide data) and downstream teams (who consume the model).
The third step for getting a comprehensive coverage is to understand your stakeholders’ concerns. The best way to increase the coverage of the issue detection component. In the context of our recommender systems, they have two major perspectives: our members and items.
Detecting production issues quickly is great but it is even better if we can predict those issues and fix them before they are in production. For example, proper cold-starting of an item (e.g. a new movie, show, or game) is important at Netflix because each item only launches once, just like Zara, after the demand is gone then a new product launches.
Once an issue is identified with either one of detection or prediction models, next phase is to find the root cause. The first step in this process is to reproduce the issue in isolation. The next step after reproducing the issue is to figure out if the issue is related to inputs of the ML model or the model itself. Once the root cause of an issue is identified, the next step is to fix the issue. This part is similar to typical software engineering: we can have a short-term hotfix or a long-term solution. Beyond fixing the issue another phase of issue resolution is improving RecSysOps itself. Finally, it is important to make RecSysOps as frictionless as possible. This makes the operations smooth and the system more reliable.
NETFLIX: A BLESSING IN DISGUISE
To conclude In this blog post I introduced RecSysOps with a set of best practices and lessons that we’ve learned at Netflix. I think these patterns are useful to consider for anyone operating a real-world recommendation system to keep it performing well and improve it over time. Overall, putting these aspects together has helped us significantly reduce issues, increased trust with our stakeholders, and allowed us to focus on innovation.
[1] Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, and D. Sculley. 2017. The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. In Proceedings of IEEE Big Data.Google Scholar
[2] Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett(Eds.). Curran Associates, Inc., 4765–4774.
Artificial intelligence, often hailed as the technological marvel of our age, has indisputably revolutionised the world as we know it. Its applications span across industries, from healthcare to finance, augmenting human capabilities and unleashing unprecedented potential. However, much like the legendary double-edged sword, AI possesses a dual nature. On one side, it brings numerous benefits, but on the other, it has the capacity to be wielded for despicable purposes. In this post, we will delve into the shadowy realm where AI’s immense power is harnessed, not for progress, but for peril.
AI-Based Threats
Artificial intelligence possesses a dark side in the realm of cybersecurity. AI-based threats leverage this technology to orchestrate malicious activities. These threats include AI-driven malware capable of adapting and evading detection such us AI-generated phishing attacks that deceive even the vigilant, and deepfake content used for social confusion, all representing the perilous side of AI’s capabilities. We should look into this topic to gain awareness of the potential threats and prepare for them.
Phishing Attacks
Phasing attacks are the most popular form of cyber attacks. It is estimated that more than 3.4 billion emails are sent every day, but with use of AI they can be taken on a new dimension. AI-driven phishing attacks involve the use of advanced algorithms to create highly convincing and personalised (language, writing style, culture, etc.) deceptive content. These sophisticated campaigns are designed to trick individuals into divulging sensitive information or taking harmful actions, making them even more challenging to detect and defend against.
Sabotaging AI
Numerous companies have either adopted AI into their operations or are in the process of doing so. It’s increasingly likely that AI will become a standard component for the majority, if not all, of companies in the near future. But this also makes AI a new target of interest for hackers, as they seek to manipulate data or inject false information that can compromise the integrity of AI-driven operations. By infiltrating AI systems, attackers could potentially exploit vulnerabilities to feed incorrect or malicious data, leading to skewed decision-making, financial losses, and reputational damage for companies relying on these technologies. As AI continues to advance, the importance of safeguarding against such manipulations becomes paramount in ensuring the reliability of AI-powered solutions.
AI Chats Recommendations
Another potential security risk involves AI-generated recommendations. When users ask AI-powered chatbots for webpage suggestions or package to solve a specific coding problem, they should exercise caution, as the responses provided by AI can frequently be outdated or don’t even exist anymore. Hackers take advantage of this by creating links or packages under links generated by AI. Once users search for specific answer they click on these fake links or install the deceptive packages, unknowingly exposing their systems to a variety of threats, including malware, spyware, or ransomware. This tactic capitalises on the trust users place in chatbots, making it essential for individuals and organisations to exercise caution and verify the authenticity of any recommendations received through these AI-driven interfaces to avoid falling victim to cyberattacks.
AI-Generated Fake Content
AI-Generated Fake Content represents a growing threat in the realm of disinformation and cyber manipulation. Hackers with malicious intent can exploit AI to create highly convincing videos and other multimedia content featuring well-known figures, such as CEOs or public figures. By harnessing the vast amounts of publicly available data, including speeches, interviews, and images, hackers can craft convincing, but entirely fabricated, messages or appearances. These fraudulent materials can be used for a variety of nefarious purposes, such as market manipulation or spreading disinformation. For instance, a hacker may create a video in which a CEO appears to announce a groundbreaking product or event, causing a surge in stock prices before the fraud is exposed. Similarly, they can flood social media platforms with posts or comments promoting fake news about wars, politicians, or other sensitive topics. The speed and scale of AI-generated content can make it challenging for individuals and organizations to discern the authenticity of the information, leaving them vulnerable to potential financial losses or reputational damage.
Conclusion
In the age of AI, we are witnessing the remarkable transformation of industries and the vast potential of artificial intelligence. However, we’ve also uncovered its darker side, where AI can be weaponised for malicious purposes. From AI-based cyber threats to the spread of fake content, the risks are real, and they can have profound consequences. To safeguard our digital landscape, it’s imperative that we prioritize data security and enact robust protective measures.
While we’ve discussed several ways hackers can misuse AI, it’s essential to remember that AI technology is ever-evolving, and we may encounter unforeseen challenges. We must prepare for the unknown, maintain vigilance, and advocate for strong government regulations to ensure the ethical and responsible use of AI. Striking a balance between innovation and security will be the key to harnessing the full potential of this transformative technology while mitigating the risks it may pose. In an age where AI’s reach continues to expand, we must always hope for the best but be prepared for the worst.
As we are all familiar with the advantages tied to data governance, and its necessities. Let’s explore the inverse side, I have asked Bard and ChatGPT for the disadvantages of extreme data governance and these are the outcomes.
(input: negative consequences of increased data governance)
Bard Increased data governance can have some negative consequences, including:
Increased costs: Implementing and maintaining a data governance program can be costly. This is especially true for large organizations with complex data environments. Reduced agility: Data governance processes can be slow and cumbersome. This can make it difficult for businesses to respond quickly to changing market conditions or customer needs. Increased complexity: Data governance can add complexity to an organization’s IT infrastructure and processes. This can make it difficult for employees to understand and follow the rules. Reduced innovation: Data governance can stifle innovation by making it difficult for employees to experiment with new data-driven ideas. Reduced employee morale: Data governance can lead to employee frustration if the rules are perceived to be too restrictive or burdensome. It is important to note that the negative consequences of increased data governance can be mitigated by careful planning and implementation. Organizations should develop a data governance program that is tailored to their specific needs and that takes into account the potential costs and risks.
Here are some tips for mitigating the negative consequences of increased data governance:
Start small: Don’t try to implement a comprehensive data governance program all at once. Start by focusing on the most important areas, such as data security and compliance. Get buy-in from stakeholders: It is important to get buy-in from all stakeholders, including business users, IT staff, and legal counsel. This will help to ensure that the data governance program is supported and that it meets the needs of the organization as a whole. Make it easy to use: Data governance rules and processes should be easy for employees to understand and follow. If the rules are too complex or burdensome, employees will find ways to work around them. Be flexible: Data governance programs should be flexible enough to adapt to changing business needs and regulations. Review and update regularly: Data governance programs should be reviewed and updated regularly to ensure that they are still effective and efficient. By taking these steps, organizations can minimize the negative consequences of increased data governance and maximize the benefits.
ChatGPT While increased data governance is generally aimed at protecting individuals’ privacy and ensuring responsible data usage, it can also have some negative consequences, especially for businesses and innovation. Here are a few potential drawbacks:
Compliance Costs: Adhering to stringent data governance regulations can be expensive. Businesses, especially smaller ones, might struggle with the costs associated with implementing new technologies and processes to ensure compliance. This can potentially lead to increased prices for products and services for consumers.
Stifled Innovation: Strict data governance regulations might deter companies from investing in innovative projects involving data, fearing legal complications and regulatory fines. This can stifle technological advancements and slow down the pace of innovation in various sectors.
Complexity and Confusion: Data governance regulations can be complex and vary from one jurisdiction to another. Companies operating globally must navigate a web of regulations, which can be confusing and challenging to comply with, especially for multinational corporations.
Reduced Data Availability: Stringent data governance may limit the availability of certain data for research and analysis purposes. This can impact scientific research, market analysis, and other data-driven fields that rely on large datasets for insights and innovations.
Impact on Small Businesses: Small businesses might find it particularly challenging to comply with complex data governance regulations. Compliance requirements can be daunting for companies with limited resources, potentially putting them at a disadvantage compared to larger competitors.
Data Fragmentation: Different regions or countries may have their own data governance laws. This can lead to data fragmentation, where data is stored in localized servers to comply with regional regulations. Data fragmentation can hinder global collaboration and the seamless exchange of information.
Overemphasis on Compliance: Focusing extensively on compliance might lead to a checkbox mentality, where companies focus on meeting the minimum requirements rather than genuinely prioritizing user privacy and data security. This can create a false sense of security for consumers.
Data Monopolies: Stricter regulations might unintentionally favor large tech companies with the resources to navigate and comply with complex regulations. Smaller competitors and startups might find it harder to compete, leading to further consolidation and potential data monopolies in certain industries.
Reduced Personalization: Data governance regulations might limit the amount of personal data companies can collect and use for customization and personalization of services. While this is aimed at protecting privacy, it could reduce the quality of personalized services and user experiences.
Slow Decision-Making: Strict data governance procedures can slow down decision-making processes within organizations. Obtaining approvals and ensuring compliance at every step can lead to delays, especially in time-sensitive situations.
It’s essential for policymakers and businesses to strike a balance between protecting user data and fostering innovation. Crafting regulations that protect privacy while allowing for responsible data usage and innovation is a challenge that requires careful consideration of the potential negative consequences.
Overall, we should strive for a balance in data governance, between too much and too little, to allow for progress and to get things done, but still create enough security and preventive measures.
Our cars are getting smarter from generation to generation, however, unfortunately, this also applies to thieves who intend to steal them. Hackers have already found vulnerabilities in electric vehicles, and their attacks can have serious consequences. As electric vehicles become more sophisticated and connected to the Internet, it is expected that the risk of hacking and cyber attacks will only increase.
One of the main problems with electric vehicles is that they are equipped with numerous sensors and controllers connected to the Internet, which makes them vulnerable to cyber attacks. Hackers could potentially gain remote access to these systems and manipulate them in a way that could cause serious harm, such as disabling brakes or changing steering. In some cases, hackers could even take control of the entire car, putting the driver and passengers at risk.
Another problem is that hackers can attack charging stations for electric vehicles. These stations are also connected to the Internet and often use wireless connectivity to connect to electric vehicles. Hackers can potentially gain access to these systems and manipulate them.
Recently, a security expert discovered a way that allows two attackers to unlock, start and drive away a Tesla Model Y electric car in a matter of seconds.
Hackers specializing in hacking Tesla electric vehicles have identified a vulnerability that allows them to hack NFC relays. However, not everything is so simple: in order to hack the system, thieves need to work in pairs and get close to the NFC chip or smartphone. Josep Pi Rodriguez of the Seattle-based computer security firm IOActive found that attackers could use Tesla’s key technology called NFC (Near field Communication) to gain control of a vehicle, designed to give car owners the ability to access them by touching an NFC card to the middle rack. Rodriguez found that if one thief approaches a critically small distance to the driver when he gets out of the car, for example, to a store or bar, and the other is standing by the car, it will be possible to open the door and start the car.
Here’s how it works: a thief standing at the car uses a special device to convince the car to send a “call” to the driver’s NFC card, but then transmits this call via Wi-Fi or Bluetooth to a mobile phone belonging to a second thief, who is watching the driver at this time. The second thief keeps this phone near the driver’s pocket or bag where the NFC card is stored, and when this NFC card responds, its signal is transmitted to the thief standing by the car via a mobile phone.
Tesla previously required drivers using an NFC card (not a keychain) to unlock their cars to place the card between the front seats in order to turn on the transmission. But after a recent software update, this requirement has been lifted. Tesla also offers the option of using a PIN code, which means that car owners must enter a four-digit code before starting the car, however, a fairly small number of owners activate it. In the end, even if this additional protection prevents thieves from leaving by car, they will still be able to use the hacking method described above to open the doors and steal any valuables inside the cabin.
To solve these problems, automakers and cybersecurity experts are working to develop stricter security measures for electric vehicles. This includes the introduction of more advanced encryption technologies, the development of secure firmware and software, as well as regular updates and patching of systems to eliminate any vulnerabilities found.
Electric car owners can also take measures to protect their cars from cyber attacks. This includes regularly updating software and firmware, using strong passwords and two-factor authentication, and not using public Wi-Fi networks when accessing the Internet from their cars.
Thus, as electric vehicles become more popular and widely used, the risk of cyber attacks and hacking is expected to increase.
This is a serious problem that could have serious consequences for electric vehicle owners, automakers and the general public. It is important that automakers and cybersecurity experts work together to develop stricter security measures for electric vehicles, as well as educate owners on the steps they can take to protect their cars from cyber attacks.
China is known for their most sophisticated surveillance systems in the world. Government uses a combination of facial recognition, biometrics, AI in order to monitor all the citizens and control them.
Face recognition software is already present in our lives, and is mainly used to unlock phones, authenticate users in mobile apps, or locate people in videos or photos.
Chinese companies are the world leaders in exporting face recognition systems. They already have 201 export deals of these biometric systems. China is also a leader in exporting AI technology in general, with 250 AI deals. These solutions can be used for various purposes: security, surveillance, identity verification.
China has huge investments in AI and biometric technologies. The government is spending billions of dollars on AI research and development. It focuses on creating new, innovative face recognition software.
The country’s advantage is also that it has a huge amount of data available for the analysis. With over 1.4 billion people, with vast access to the internet, and huge surveillance system, China has a great source to train and test their solutions. This allowed them to make very reliable face recognition algorithms, making them a leader in this field.
Here you can watch how the system works:
With that big dominance comes also a number of challenges when trying to expand to other markets. There is a huge concern about privacy, ethics, and protection of using biometrics for control and surveillance. The government actively promotes the use of biometrics as it improves national security and public safety, but also provides more efficient services to citizens.
Chinese surveillance system is a powerful tool with potential to enhance public safety. However, we should be mindful about the potential risks of using such systems, and we have to make sure that it is really used to protect human rights. Chinese government has to take steps to increase transparency of their solutions and ensure people that they use it responsibly.
Methane is a chemical compound with the molecular formula CH4. It is the primary component of a natural gas. It is also the simplest alkane possible. This colourless and odourless gas is combustible and has a very unusual boiling point, because it is only -162°C. You can find methane in many natural sources like termites, wetlands or marshes. It can also be found in human activities such as extracting fossil fuels and raising livestock. You can also call it a potent greenhouse gas which has a 28 times greater global warming potential than carbon dioxide.
Methane leakage, can have a diversity of negative impacts on the environment as well as on human health. The impact of methane leaks on climate change is substantial as it is a potent greenhouse. When it leaks into the atmosphere, it traps heat and contributes to rising temperatures. This can cause many consequences, for instance, rising sea levels or extreme weather happening more often. Methane leakage can also pose a danger of explosions and fires considering the fact that methane is highly flammable. Accumulation of this gas in an enclosed space can easily lead to an explosion or fire which then can lead to property damage or injury to people. Therefore detecting and repairing methane leaks as soon as possible is crucial to minimising its devastating results.
Detecting methane leaks has always been a challenging task. Methane is difficult to detect because it is odourless, colourless and tasteless. Thus, traditional methane leaks detecting methods rely on visual inspections and sniffer dogs, which can be pricey and time consuming. Nevertheless AI has been making a great progress in development lately and with the advancements in the technology there are some new methods for detecting methane leaks becoming available.
GCI (Gas Cloud Imaging) systems are advanced tools for detecting and monitoring methane leakage in the oil and gas industry. They are able to detect and identify over 50 gases using infrared cameras. The cameras detect the leak and show it as a visual cloud, a contrast against the background. They take a picture and send an alert by text or email. The operator can then use the information and approve if the system detected a leak or made a mistake. All alarms and cameras footage ale saved and stored, so the scientists group can analyse it and establish the cause of the leak later. The biggest GCI’s benefit is that the system is able to detect leaks very quickly and it is surprisingly accurate in what it does. Those attributes significantly reduce the amount of the gas released into the atmosphere. Also the key benefit is that the system covers/scans very big areas in a short period of time, what makes the system even more efficient and effective. Additionally you those cameras could be used in multiple cases. They not only detect methane leaks but they also have fire and intrusion detection built in.
In conclusion methane leaks can have intense impact on the environment and human health. It is vital that we take decisive steps towards reducing and minimising the amount of methane emitted by repairing its leaks as soon as we detect it. We need advanced tools to detect it in there first place and AI has a great potential in becoming a very important tool to help us do that. GCI systems can be an essential tool for monitoring and detecting methane leaks. Thanks to them, we’re given quick and reliable results that we’ll need in order to protect and help the environment, whilst reducing the impacts of greenhouse gas emissions.
