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Wipro Ltd
Business CISO Cyber Security Generative AI Wipro Ltd

Why Every Business is Scrambling to Hire Cybersecurity Experts


 

The cybersecurity arena is developing at a breakneck pace, creating a significant talent shortage across the industry. This challenge was highlighted by Saugat Sindhu, Senior Partner and Global Head of Advisory Services at Wipro Ltd. He emphasised the pressing need for skilled cybersecurity professionals, noting that the rapid advancements in technology make it difficult for the industry to keep up.


Cybersecurity: A Business Enabler

Over the past decade, cybersecurity has transformed from a corporate function to a crucial business enabler. Sindhu pointed out that cybersecurity is now essential for all companies, not just as a compliance measure but as a strategic asset. Businesses, clients, and industries understand that neglecting cybersecurity can give competitors an advantage, making robust cybersecurity practices indispensable.

The role of the Chief Information Security Officer (CISO) has also evolved. Today, CISOs are responsible for ensuring that businesses have the necessary tools and technologies to grow securely. This includes minimising outages and reputational damage from cyber incidents. According to Sindhu, modern CISOs are more about enabling business operations rather than restricting them.

Generative AI is one of the latest disruptors in the cybersecurity field, much like the cloud was a decade ago. Sindhu explained that different sectors face varying levels of risk with AI adoption. For instance, healthcare, manufacturing, and financial services are particularly vulnerable to attacks like data poisoning, model inversions, and supply chain vulnerabilities. Ensuring the security of AI models is crucial, as vulnerabilities can lead to severe backdoor attacks.

At Wipro, cybersecurity is a top priority, involving multiple departments including the audit office, risk office, core security office, and IT office. Sindhu stated that cybersecurity considerations are now integrated into the onset of any technology transformation project, rather than being an afterthought. This proactive approach ensures that adequate controls are in place from the beginning.

Wipro is heavily investing in cybersecurity training for its employees and practitioners. The company collaborates with major universities in India to support training courses, making it easier to attract new talent. Sindhu emphasised the importance of continuous education and certification to keep up with the fast-paced changes in the field.

Wipro's commitment to cybersecurity is evident in its robust infrastructure. The company boasts over 9,000 cybersecurity specialists and operates 12 global cyber defence centres across more than 60 countries. This extensive network underscores Wipro's dedication to maintaining high security standards and addressing cyber risks proactively.

The rapid evolution of cybersecurity presents pivotal challenges, but also underscores the importance of viewing it as a business enabler. With the right training, proactive measures, and integrated approaches, companies like Wipro are striving to stay ahead of threats and ensure robust protection for their clients. As the demand for cybersecurity talent continues to grow, ongoing education and collaboration will be key to bridging the skills gap.



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Technology
antivirus software Artifcial Intelligence CISOs Cyber Threats Generative AI Technology

AI Brings A New Era of Cyber Threats – Are We Ready?

 



Cyberattacks are becoming alarmingly frequent, with a new attack occurring approximately every 39 seconds. These attacks, ranging from phishing schemes to ransomware, have devastating impacts on businesses worldwide. The cost of cybercrime is projected to hit $9.5 trillion in 2024, and with AI being leveraged by cybercriminals, this figure is likely to rise.

According to a recent RiverSafe report surveying Chief Information Security Officers (CISOs) in the UK, one in five CISOs identifies AI as the biggest cyber threat. The increasing availability and sophistication of AI tools are empowering cybercriminals to launch more complex and large-scale attacks. The National Cyber Security Centre (NCSC) warns that AI will significantly increase the volume and impact of cyberattacks, including ransomware, in the near future.

AI is enhancing traditional cyberattacks, making them more difficult to detect. For example, AI can modify malware to evade antivirus software. Once detected, AI can generate new variants of the malware, allowing it to persist undetected, steal data, and spread within networks. Additionally, AI can bypass firewalls by creating legitimate-looking traffic and generating convincing phishing emails and deepfakes to deceive victims into revealing sensitive information.

Policies to Mitigate AI Misuse

AI misuse is not only a threat from external cybercriminals but also from employees unknowingly putting company data at risk. One in five security leaders reported experiencing data breaches due to employees sharing company data with AI tools like ChatGPT. These tools are popular for their efficiency, but employees often do not consider the security risks when inputting sensitive information.

In 2023, ChatGPT experienced an extreme data breach, highlighting the risks associated with generative AI tools. While some companies have banned the use of such tools, this is a short-term solution. The long-term approach should focus on education and implementing carefully managed policies to balance the benefits of AI with security risks.

The Growing Threat of Insider Risks

Insider threats are a significant concern, with 75% of respondents believing they pose a greater risk than external threats. Human error, often due to ignorance or unintentional mistakes, is a leading cause of data breaches. These threats are challenging to defend against because they can originate from employees, contractors, third parties, and anyone with legitimate access to systems.

Despite the known risks, 64% of CISOs stated their organizations lack sufficient technology to protect against insider threats. The rise in digital transformation and cloud infrastructure has expanded the attack surface, making it difficult to maintain appropriate security measures. Additionally, the complexity of digital supply chains introduces new vulnerabilities, with trusted business partners responsible for up to 25% of insider threat incidents.

Preparing for AI-Driven Cyber Threats

The evolution of AI in cyber threats necessitates a revamp of cybersecurity strategies. Businesses must update their policies, best practices, and employee training to mitigate the potential damages of AI-powered attacks. With both internal and external threats on the rise, organisations need to adapt to the new age of cyber threats to protect their valuable digital assets effectively.




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Technology
AI Chatbot Generative AI Google Gemini Open AI Technology

From Text to Action: Chatbots in Their Stone Age

From Text to Action: Chatbots in Their Stone Age

The stone age of AI

Despite all the talk of generative AI disrupting the world, the technology has failed to significantly transform white-collar jobs. Workers are experimenting with chatbots for activities like email drafting, and businesses are doing numerous experiments, but office work has yet to experience a big AI overhaul.

Chatbots and their limitations

That could be because we haven't given chatbots like Google's Gemini and OpenAI's ChatGPT the proper capabilities yet; they're typically limited to taking in and spitting out text via a chat interface.

Things may become more fascinating in commercial settings when AI businesses begin to deploy so-called "AI agents," which may perform actions by running other software on a computer or over the internet.

Tool use for AI

Anthropic, a rival of OpenAI, unveiled a big new product today that seeks to establish the notion that tool use is required for AI's next jump in usefulness. The business is allowing developers to instruct its chatbot Claude to use external services and software to complete more valuable tasks. 

Claude can, for example, use a calculator to solve math problems that vex big language models; be asked to visit a database storing customer information; or be forced to use other programs on a user's computer when it would be beneficial.

Anthropic has been assisting various companies in developing Claude-based aides for their employees. For example, the online tutoring business Study Fetch has created a means for Claude to leverage various platform tools to customize the user interface and syllabus content displayed to students.

Other businesses are also joining the AI Stone Age. At its I/O developer conference earlier this month, Google showed off a few prototype AI agents, among other new AI features. One of the agents was created to handle online shopping returns by searching for the receipt in the customer's Gmail account, completing the return form, and scheduling a package pickup.

Challenges and caution

  • While tool use is exciting, it comes with challenges. Language models, including large ones, don’t always understand context perfectly.
  • Ensuring that AI agents behave correctly and interpret user requests accurately remains a hurdle.
  • Companies are cautiously exploring these capabilities, aware of the potential pitfalls.

The Next Leap

The Stone Age of chatbots represents a significant leap forward. Here’s what we can expect:

Action-oriented chatbots

  • Chatbots that can interact with external services will be more useful. Imagine a chatbot that books flights, schedules meetings, or orders groceries—all through seamless interactions.
  • These chatbots won’t be limited to answering questions; they’ll take action based on user requests.

Enhanced Productivity

  • As chatbots gain tool-using abilities, productivity will soar. Imagine a virtual assistant that not only schedules your day but also handles routine tasks.
  • Businesses can benefit from AI agents that automate repetitive processes, freeing up human resources for more strategic work.

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Technology
Business Security ChatGPT Data Privacy Generative AI Technology

Risks of Generative AI for Organisations and How to Manage Them

 

Employers should be aware of the potential data protection issues before experimenting with generative AI tools like ChatGPT. You can't just feed human resources data into a generative AI tool because of the rise in privacy and data protection laws in the US, Europe, and other countries in recent years. After all, employee data—including performance, financial, and even health data—is often quite sensitive.

Obviously, this is an area where companies should seek legal advice. It's also a good idea to consult with an AI expert regarding the ethics of utilising generative AI (to ensure that you're acting not only legally, but also ethically and transparently). But, as a starting point, here are two major factors that employers should be aware of. 

Feeding personal data

As I previously stated, employee data is often highly sensitive and sensitive. It is precisely the type of data that, depending on your jurisdiction, is usually subject to the most stringent forms of legal protection.

This makes it highly dangerous to feed such data into a generative AI tool. Why? Because many generative AI technologies use the information provided to fine-tune the underlying language model. In other words, it may use the data you provide for training purposes, and it may eventually expose that information to other users. So, suppose you employ a generative AI tool to generate a report on employee salary based on internal employee information. In the future, the AI tool can employ the data to generate responses for other users (outside of your organisation). Personal information could easily be absorbed by the generative AI tool and reused. 

This isn't as shady as it sounds. Many generative AI programmes' terms and conditions explicitly specify that data provided to the AI may be utilised for training and fine-tuning or revealed when users request cases of previously submitted inquiries. As a result, when you agree to the terms of service, always make sure you understand exactly what you're getting yourself into. Experts urge that any data given to a generative AI service be anonymised and free of personally identifiable information. This is frequently referred to as "de-identifying" the data.

Risks of generative AI outputs 

There are risks associated with the output or content developed by generative AIs, in addition to the data fed into them. In particular, there is a possibility that the output from generative AI technologies will be based on personal data acquired and handled in violation of data privacy laws. 

For example, suppose you ask a generative AI tool to provide a report on average IT salary in your area. There is a possibility that the programme will scrape personal data from the internet without your authorization, violating data protection rules, and then serve it to you. Employers who exploit personal data provided by a generative AI tool may be held liable for data protection violations. For the time being, it is a legal grey area, with the generative AI provider likely bearing the most or all of the duty, but the risk remains. 

Cases like this are already appearing. Indeed, one lawsuit claims that ChatGPT was trained on "massive amounts of personal data," such as medical records and information about children, that was accessed without consent. You do not want your organisation to become unwittingly involved in a litigation like this. Essentially, we're discussing an "inherited" risk of violating data protection regulations. However, there is a risk involved. 

The way forward

Employers must carefully evaluate the data protection and privacy consequences of utilising generative AI and seek expert assistance. However, don't let this put you off adopting generative AI altogether. Generative AI, when used properly and within the bounds of the law, can be an extremely helpful tool for organisations.
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Spear Phishing
AI threats Business Email Compromise CISOs Cyber Security Email Safety Generative AI Impersonation online threats Security Policies Spear Phishing

Adapting Cybersecurity Policies to Combat AI-Driven Threats

 

Over the last few years, the landscape of cyber threats has significantly evolved. The once-common traditional phishing emails, marked by obvious language errors, clear malicious intent, and unbelievable narratives, have seen a decline. Modern email security systems can easily detect these rudimentary attacks, and recipients have grown savvy enough to recognize and ignore them. Consequently, this basic form of phishing is quickly becoming obsolete. 

However, as traditional phishing diminishes, a more sophisticated and troubling threat has emerged. Cybercriminals are now leveraging advanced generative AI (GenAI) tools to execute complex social engineering attacks. These include spear-phishing, VIP impersonation, and business email compromise (BEC). In light of these developments, Chief Information Security Officers (CISOs) must adapt their cybersecurity strategies and implement new, robust policies to address these advanced threats. One critical measure is implementing segregation of duties (SoD) in handling sensitive data and assets. 

For example, any changes to bank account information for invoices or payroll should require approval from multiple individuals. This multi-step verification process ensures that even if one employee falls victim to a social engineering attack, others can intercept and prevent fraudulent actions. Regular and comprehensive security training is also crucial. Employees, especially those handling sensitive information and executives who are prime targets for BEC, should undergo continuous security education. 

This training should include live sessions, security awareness videos, and phishing simulations based on real-world scenarios. By investing in such training, employees can become the first line of defense against sophisticated cyber threats. Additionally, gamifying the training process—such as rewarding employees for reporting phishing attempts—can boost engagement and effectiveness. Encouraging a culture of reporting suspicious emails is another essential policy. 

Employees should be urged to report all potentially malicious emails rather than simply deleting or ignoring them. This practice allows the Security Operations Center (SOC) team to stay informed about ongoing threats and enhances organizational security awareness. Clear policies should emphasize that it's better to report false positives than to overlook potential threats, fostering a vigilant and cautious organizational culture. To mitigate social engineering risks, organizations should restrict access to sensitive information on a need-to-know basis. 

Simple policy changes, like keeping company names private in public job listings, can significantly reduce the risk of social engineering attacks. Limiting the availability of organizational details helps prevent cybercriminals from gathering the information needed to craft convincing attacks. Given the rapid advancements in generative AI, it's imperative for organizations to adopt adaptive security systems. Shifting from static to dynamic security measures, supported by AI-enabled defensive tools, ensures that security capabilities remain effective against evolving threats. 

This proactive approach helps organizations stay ahead of the latest attack vectors. The rise of generative AI has fundamentally changed the field of cybersecurity. In a short time, these technologies have reshaped the threat landscape, making it essential for CISOs to continuously update their strategies. Effective, current policies are vital for maintaining a strong security posture. 

This serves as a starting point for CISOs to refine and enhance their cybersecurity policies, ensuring they are prepared for the challenges posed by AI-driven threats. In this ever-changing environment, staying ahead of cybercriminals requires constant vigilance and adaptation.
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Technology
Artifcial Intelligence Generative AI Machine learning Predictive AI Technology

Predictive AI: What Do We Need to Understand?


We all are no strangers to artificial intelligence (AI) expanding over our lives, but Predictive AI stands out as uncharted waters. What exactly fuels its predictive prowess, and how does it operate? Let's take a detailed exploration of Predictive AI, unravelling its intricate workings and practical applications.

What Is Predictive AI?

Predictive AI operates on the foundational principle of statistical analysis, using historical data to forecast future events and behaviours. Unlike its creative counterpart, Generative AI, Predictive AI relies on vast datasets and advanced algorithms to draw insights and make predictions. It essentially sifts through heaps of data points, identifying patterns and trends to inform decision-making processes.

At its core, Predictive AI thrives on "big data," leveraging extensive datasets to refine its predictions. Through the iterative process of machine learning, Predictive AI autonomously processes complex data sets, continuously refining its algorithms based on new information. By discerning patterns within the data, Predictive AI offers invaluable insights into future trends and behaviours.


How Does It Work?

The operational framework of Predictive AI revolves around three key mechanisms:

1. Big Data Analysis: Predictive AI relies on access to vast quantities of data, often referred to as "big data." The more data available, the more accurate the analysis becomes. It sifts through this data goldmine, extracting relevant information and discerning meaningful patterns.

2. Machine Learning Algorithms: Machine learning serves as the backbone of Predictive AI, enabling computers to learn from data without explicit programming. Through algorithms that iteratively learn from data, Predictive AI can autonomously improve its accuracy and predictive capabilities over time.

3. Pattern Recognition: Predictive AI excels at identifying patterns within the data, enabling it to anticipate future trends and behaviours. By analysing historical data points, it can discern recurring patterns and extrapolate insights into potential future outcomes.


Applications of Predictive AI

The practical applications of Predictive AI span a number of industries, revolutionising processes and decision-making frameworks. From cybersecurity to finance, weather forecasting to personalised recommendations, Predictive AI is omnipresent, driving innovation and enhancing operational efficiency.


Predictive AI vs Generative AI

While Predictive AI focuses on forecasting future events based on historical data, Generative AI takes a different approach by creating new content or solutions. Predictive AI uses machine learning algorithms to analyse past data and identify patterns for predicting future outcomes. In contrast, Generative AI generates new content or solutions by learning from existing data patterns but doesn't necessarily focus on predicting future events. Essentially, Predictive AI aims to anticipate trends and behaviours, guiding decision-making processes, while Generative AI fosters creativity and innovation, generating novel ideas and solutions. This distinction highlights the complementary roles of both AI approaches in driving progress and innovation across various domains.

Predictive AI acts as a proactive defence system in cybersecurity, spotting and stopping potential threats before they strike. It looks at how users behave and any unusual activities in systems to make digital security stronger, protecting against cyber attacks.

Additionally, Predictive AI helps create personalised recommendations and content on consumer platforms. Studying what users like and how they interact provides customised experiences, making users happier and more engaged.

The bottom line is its ability to forecast future events and behaviours based on historical data heralds a new era of data-driven decision-making and innovation. 




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Technology
Artificial Intelligence Business AI Financial Institution Generative AI Technology

AI Could Be As Impactful as Electricity, Predicts Jamie Dimon

 

Jamie Dimon might be concerned about the economy, but he's optimistic regarding artificial intelligence.

In his annual shareholder letter, JP Morgan Chase's (JPM) CEO stated that he believes the effects of AI on business, society, and the economy would be not just significant, but also life-changing. 

Dimon stated, we are fully convinced that the consequences of AI will be extraordinary and possibly as transformational as some of the major technological inventions of the past several hundred years: Think of the printing press, the steam engine, electricity, computing, and the Internet, among others. However, we do not know the full effect or the precise rate at which AI will change our business — or how it will affect society at large. 

Since the financial institution has been employing AI for over a decade, more than 2,000 data scientists and experts in AI and machine learning are employed there, according to Dimon. More than 400 use cases involving the technology are in the works, and they include fraud, risk, and marketing. 

“We're also exploring the potential that generative AI (GenAI) can unlock across a range of domains, most notably in software engineering, customer service and operations, as well as in general employee productivity,” Dimon added. “In the future, we envision GenAI helping us reimagine entire business workflows.”

JP Morgan is capitalising on its interest in artificial intelligence, advertising for almost 3,600 AI-related jobs last year, nearly twice as many as Citigroup, which had the second largest number of financial service industry ads (2,100). Deutsche Bank and BNP Paribas both advertised for little over 1,000 AI posts. 

JP Morgan is developing a ChatGPT-like service to assist consumers in making investing decisions. The company trademarked IndexGPT in May, stating that it would use "cloud computing software using artificial intelligence" for "analysing and selecting securities tailored to customer needs." 

Dimon has long advocated for artificial intelligence, stating earlier this year that the technology "can do things that the human mind simply cannot do." 

While Dimon is upbeat regarding the bank's future with AI, he also stated in his letter that the company is not disregarding the technology's potential risks.
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Technology
AI technology AI tools Generative AI generative AI tools innovative features Technology

What AI Can Do Today? The latest generative AI tool to find the perfect AI solution for your tasks

 

Generative AI tools have proliferated in recent times, offering a myriad of capabilities to users across various domains. From ChatGPT to Microsoft's Copilot, Google's Gemini, and Anthrophic's Claude, these tools can assist with tasks ranging from text generation to image editing and music composition.
 
The advent of platforms like ChatGPT Plus has revolutionized user experiences, eliminating the need for logins and providing seamless interactions. With the integration of advanced features like Dall-E image editing support, these AI models have become indispensable resources for users seeking innovative solutions. 

However, the sheer abundance of generative AI tools can be overwhelming, making it challenging to find the right fit for specific tasks. Fortunately, websites like What AI Can Do Today serve as invaluable resources, offering comprehensive analyses of over 5,800 AI tools and cataloguing over 30,000 tasks that AI can perform. 

Navigating What AI Can Do Today is akin to using a sophisticated search engine tailored specifically for AI capabilities. Users can input queries and receive curated lists of AI tools suited to their requirements, along with links for immediate access. 

Additionally, the platform facilitates filtering by category, further streamlining the selection process. While major AI models like ChatGPT and Copilot are adept at addressing a wide array of queries, What AI Can Do Today offers a complementary approach, presenting users with a diverse range of options and allowing for experimentation and discovery. 

By leveraging both avenues, users can maximize their chances of finding the most suitable solution for their needs. Moreover, the evolution of custom GPTs, supported by platforms like ChatGPT Plus and Copilot, introduces another dimension to the selection process. These specialized models cater to specific tasks, providing tailored solutions and enhancing efficiency. 

It's essential to acknowledge the inherent limitations of generative AI tools, including the potential for misinformation and inaccuracies. As such, users must exercise discernment and critically evaluate the outputs generated by these models. 

Ultimately, the journey to finding the right generative AI tool is characterized by experimentation and refinement. While seasoned users may navigate this landscape with ease, novices can rely on resources like What AI Can Do Today to guide their exploration and facilitate informed decision-making. 

The ecosystem of generative AI tools offers boundless opportunities for innovation and problem-solving. By leveraging platforms like ChatGPT, Copilot, Gemini, Claude, and What AI Can Do Today, users can unlock the full potential of AI and harness its transformative capabilities.
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Online Security
CISOs Cyber Security Data Privacy Digital Literacy Generative AI IP addresses Online Security

What Are The Risks of Generative AI?

 




We are all drowning in information in this digital world and the widespread adoption of artificial intelligence (AI) has become increasingly commonplace within various spheres of business. However, this technological evolution has brought about the emergence of generative AI, presenting a myriad of cybersecurity concerns that weigh heavily on the minds of Chief Information Security Officers (CISOs). Let's synthesise this issue and see the intricacies from a microscopic light.

Model Training and Attack Surface Vulnerabilities:

Generative AI collects and stores data from various sources within an organisation, often in insecure environments. This poses a significant risk of data access and manipulation, as well as potential biases in AI-generated content.


Data Privacy Concerns:

The lack of robust frameworks around data collection and input into generative AI models raises concerns about data privacy. Without enforceable policies, there's a risk of models inadvertently replicating and exposing sensitive corporate information, leading to data breaches.


Corporate Intellectual Property (IP) Exposure:

The absence of strategic policies around generative AI and corporate data privacy can result in models being trained on proprietary codebases. This exposes valuable corporate IP, including API keys and other confidential information, to potential threats.


Generative AI Jailbreaks and Backdoors:

Despite the implementation of guardrails to prevent AI models from producing harmful or biased content, researchers have found ways to circumvent these safeguards. Known as "jailbreaks," these exploits enable attackers to manipulate AI models for malicious purposes, such as generating deceptive content or launching targeted attacks.


Cybersecurity Best Practices:

To mitigate these risks, organisations must adopt cybersecurity best practices tailored to generative AI usage:

1. Implement AI Governance: Establishing governance frameworks to regulate the deployment and usage of AI tools within the organisation is crucial. This includes transparency, accountability, and ongoing monitoring to ensure responsible AI practices.

2. Employee Training: Educating employees on the nuances of generative AI and the importance of data privacy is essential. Creating a culture of AI knowledge and providing continuous learning opportunities can help mitigate risks associated with misuse.

3. Data Discovery and Classification: Properly classifying data helps control access and minimise the risk of unauthorised exposure. Organisations should prioritise data discovery and classification processes to effectively manage sensitive information.

4. Utilise Data Governance and Security Tools: Employing data governance and security tools, such as Data Loss Prevention (DLP) and threat intelligence platforms, can enhance data security and enforcement of AI governance policies.


Various cybersecurity vendors provide solutions tailored to address the unique challenges associated with generative AI. Here's a closer look at some of these promising offerings:

1. Google Cloud Security AI Workbench: This solution, powered by advanced AI capabilities, assesses, summarizes, and prioritizes threat data from both proprietary and public sources. It incorporates threat intelligence from reputable sources like Google, Mandiant, and VirusTotal, offering enterprise-grade security and compliance support.

2. Microsoft Copilot for Security: Integrated with Microsoft's robust security ecosystem, Copilot leverages AI to proactively detect cyber threats, enhance threat intelligence, and automate incident response. It simplifies security operations and empowers users with step-by-step guidance, making it accessible even to junior staff members.

3. CrowdStrike Charlotte AI: Built on the Falcon platform, Charlotte AI utilizes conversational AI and natural language processing (NLP) capabilities to help security teams respond swiftly to threats. It enables users to ask questions, receive answers, and take action efficiently, reducing workload and improving overall efficiency.

4. Howso (formerly Diveplane): Howso focuses on advancing trustworthy AI by providing AI solutions that prioritize transparency, auditability, and accountability. Their Howso Engine offers exact data attribution, ensuring traceability and accountability of influence, while the Howso Synthesizer generates synthetic data that can be trusted for various use cases.

5. Cisco Security Cloud: Built on zero-trust principles, Cisco Security Cloud is an open and integrated security platform designed for multicloud environments. It integrates generative AI to enhance threat detection, streamline policy management, and simplify security operations with advanced AI analytics.

6. SecurityScorecard: SecurityScorecard offers solutions for supply chain cyber risk, external security, and risk operations, along with forward-looking threat intelligence. Their AI-driven platform provides detailed security ratings that offer actionable insights to organizations, aiding in understanding and improving their overall security posture.

7. Synthesis AI: Synthesis AI offers Synthesis Humans and Synthesis Scenarios, leveraging a combination of generative AI and cinematic digital general intelligence (DGI) pipelines. Their platform programmatically generates labelled images for machine learning models and provides realistic security simulation for cybersecurity training purposes.

These solutions represent a diverse array of offerings aimed at addressing the complex cybersecurity challenges posed by generative AI, providing organizations with the tools needed to safeguard their digital assets effectively.

While the adoption of generative AI presents immense opportunities for innovation, it also brings forth significant cybersecurity challenges. By implementing robust governance frameworks, educating employees, and leveraging advanced security solutions, organisations can navigate these risks and harness the transformative power of AI responsibly.

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Technology
CPU Generative AI GPUs NVIDIA Online Security Technology

Are GPUs Ready for the AI Security Test?

 


As generative AI technology gains momentum, the focus on cybersecurity threats surrounding the chips and processing units driving these innovations intensifies. The crux of the issue lies in the limited number of manufacturers producing chips capable of handling the extensive data sets crucial for generative AI systems, rendering them vulnerable targets for malicious attacks.

According to recent records, Nvidia, a leading player in GPU technology, announced cybersecurity partnerships during its annual GPU technology conference. This move underscores the escalating concerns within the industry regarding the security of chips and hardware powering AI technologies.

Traditionally, cyberattacks garner attention for targeting software vulnerabilities or network flaws. However, the emergence of AI technologies presents a new dimension of threat. Graphics processing units (GPUs), integral to the functioning of AI systems, are susceptible to similar security risks as central processing units (CPUs).


Experts highlight four main categories of security threats facing GPUs:


1. Malware attacks, including "cryptojacking" schemes where hackers exploit processing power for cryptocurrency mining.

2. Side-channel attacks, exploiting data transmission and processing flaws to steal information.

3. Firmware vulnerabilities, granting unauthorised access to hardware controls.

4. Supply chain attacks, targeting GPUs to compromise end-user systems or steal data.


Moreover, the proliferation of generative AI amplifies the risk of data poisoning attacks, where hackers manipulate training data to compromise AI models.

Despite documented vulnerabilities, successful attacks on GPUs remain relatively rare. However, the stakes are high, especially considering the premium users pay for GPU access. Even a minor decrease in functionality could result in significant losses for cloud service providers and customers.

In response to these challenges, startups are innovating AI chip designs to enhance security and efficiency. For instance, d-Matrix's chip partitions data to limit access in the event of a breach, ensuring robust protection against potential intrusions.

As discussions surrounding AI security evolve, there's a growing recognition of the need to address hardware and chip vulnerabilities alongside software concerns. This shift reflects a proactive approach to safeguarding AI technologies against emerging threats.

The intersection of generative AI and GPU technology highlights the critical importance of cybersecurity in the digital age. By understanding and addressing the complexities of GPU security, stakeholders can mitigate risks and foster a safer environment for AI innovation and adoption.


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SaaS Apps
BEC Attacks Cyber Attacks Cybersecurity Generative AI SaaS Apps

AI Might Be Paving The Way For Cyber Attacks

 


In a recent eye-opening report from cybersecurity experts at Perception Point, a major spike in sneaky online attacks has been uncovered. These attacks, called Business Email Compromise (BEC), zoomed up by a whopping 1,760% in 2023. The bad actors behind these attacks are using fancy tech called generative AI (GenAI) to craft tricky emails that pretend to be from big-shot companies and bosses. These fake messages trick people into giving away important information or even money, putting both companies and people at serious risk.

The report highlights a dramatic escalation in BEC attacks, from a mere 1% of cyber threats in 2022 to a concerning 18.6% in 2023. Cybercriminals now employ sophisticated emails crafted through generative AI, impersonating reputable companies and executives. This deceptive tactic dupes unsuspecting victims into surrendering sensitive data or funds, posing a significant threat to organisational security and financial stability.

Exploiting the capabilities of AI technology, cybercriminals have embraced GenAI to orchestrate intricate and deceptive attacks. BEC attacks have become a hallmark of this technological advancement, presenting a formidable challenge to cybersecurity experts worldwide.

Beyond BEC attacks, the report sheds light on emerging threat vectors employed by cybercriminals to bypass traditional security measures. Malicious QR codes, known as “quishing,” have seen a considerable uptick, comprising 2.7% of all phishing attacks. Attackers exploit users’ trust in these seemingly innocuous symbols by leveraging QR codes to conceal malicious sites.

Additionally, the report reveals a concerning trend known as “two-step phishing,” witnessing a 175% surge in 2023. This tactic capitalises on legitimate services and websites to evade detection, exploiting the credibility of well-known domains. Cybercriminals circumvent conventional security protocols with alarming efficacy by directing users to a genuine site before redirecting them to a malicious counterpart.

The urgent need for enhanced security measures cannot be emphasised more as cyber threats evolve in sophistication and scale. Organisations must prioritise advanced security solutions to safeguard their digital assets. With one in every five emails deemed illegitimate and phishing attacks comprising over 70% of all threats, the imperative for robust email security measures has never been clearer.

Moreover, the widespread adoption of web-based productivity tools and Software-as-a-Service (SaaS) applications has expanded the attack surface, necessitating comprehensive browser security and data governance strategies. Addressing vulnerabilities within these digital ecosystems is paramount to mitigating the risk of data breaches and financial loss.

Perception Point’s Annual Report highlights the urgent need for proactive cybersecurity measures in the face of evolving cyber threats. As cybercriminals leverage technological advancements to perpetrate increasingly sophisticated attacks, organisations must remain vigilant and implement robust security protocols to safeguard against potential breaches. By embracing innovative solutions and adopting a proactive stance towards cybersecurity, businesses can bolster their defences and protect against the growing menace of BEC attacks and other malicious activities. Stay informed, stay secure.


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Technology
AI Worms Generative AI Google OpenAI Technology

Generative AI Worms: Threat of the Future?

Generative AI worms

The generative AI systems of the present are becoming more advanced due to the rise in their use, such as Google's Gemini and OpenAI's ChatGPT. Tech firms and startups are making AI bits and ecosystems that can do mundane tasks on your behalf, think about blocking a calendar or product shopping. But giving more freedom to these things tools comes at the cost of risking security. 

Generative AI worms: Threat in the future

In the latest study, researchers have made the first "generative AI worms" that can spread from one device to another, deploying malware or stealing data in the process.  

Nassi, in collaboration with fellow academics Stav Cohen and Ron Bitton, developed the worm, which they named Morris II in homage to the 1988 internet debacle caused by the first Morris computer worm. The researchers demonstrate how the AI worm may attack a generative AI email helper to steal email data and send spam messages, circumventing several security measures in ChatGPT and Gemini in the process, in a research paper and website.

Generative AI worms in the lab

The study, conducted in test environments rather than on a publicly accessible email assistant, coincides with the growing multimodal nature of large language models (LLMs), which can produce images and videos in addition to text.

Prompts are language instructions that direct the tools to answer a question or produce an image. This is how most generative AI systems operate. These prompts, nevertheless, can also be used as a weapon against the system. 

Prompt injection attacks can provide a chatbot with secret instructions, while jailbreaks can cause a system to ignore its security measures and spew offensive or harmful content. For instance, a hacker might conceal text on a website instructing an LLM to pose as a con artist and request your bank account information.

The researchers used a so-called "adversarial self-replicating prompt" to develop the generative AI worm. According to the researchers, this prompt causes the generative AI model to output a different prompt in response. 

The email system to spread worms

The researchers connected ChatGPT, Gemini, and open-source LLM, LLaVA, to develop an email system that could send and receive messages using generative AI to demonstrate how the worm may function. They then discovered two ways to make use of the system: one was to use a self-replicating prompt that was text-based, and the other was to embed the question within an image file.

A video showcasing the findings shows the email system repeatedly forwarding a message. Also, according to the experts, data extraction from emails is possible. According to Nassi, "It can be names, phone numbers, credit card numbers, SSNs, or anything else that is deemed confidential."

Generative AI worms to be a major threat soon

Nassi and the other researchers report that they expect to see generative AI worms in the wild within the next two to three years in a publication that summarizes their findings. According to the research paper, "many companies in the industry are massively developing GenAI ecosystems that integrate GenAI capabilities into their cars, smartphones, and operating systems."


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Generative AI Revolutionizing Indian Fintech

 

Over the past decade, the fintech industry in India has seen remarkable growth, becoming a leading force in driving significant changes. This sector has brought about a revolution in financial transactions, investments, and accessibility to products by integrating advanced technologies like artificial intelligence (AI), blockchain, and data analytics.

The swift adoption of these cutting-edge technologies has propelled the industry's growth trajectory, with forecasts suggesting a potential trillion-dollar valuation by 2030. As fintech continues to evolve, it's clear that automation and AI, particularly Generative AI, are reshaping the landscape of online trading and investment, promising heightened productivity and efficiency.

Recent market studies indicate substantial growth potential for Generative AI in India's financial market, particularly in investing and trading segments. By 2032, the market size for Generative AI in investing is expected to reach around INR 9101 Cr, a significant rise from INR 705.6 Cr in 2022. Similarly, the market size for Generative AI in trading is projected to reach about INR 11.76K Cr by 2032, compared to INR 1294.1 Cr in 2022. These projections underscore the transformative impact and growing importance of Generative AI in shaping the future of online trading and investment in India.

Generative AI, a subset of AI, is emerging as a game-changer in online trading by using algorithms to generate data and make predictive forecasts. This technology enables traders to simulate various market conditions, predict outcomes, and develop robust trading strategies. By leveraging historical and synthetic data, Generative AI-powered tools not only analyze past market trends but also generate synthetic data to explore hypothetical scenarios and test strategies in a risk-free environment. Additionally, Generative AI helps identify patterns within large datasets, providing traders with valuable insights for making informed investment decisions in dynamic market environments.

Predictive Analytics and Market Insights

Generative AI algorithms excel in predictive analytics, offering precise forecasts of future market trends by analyzing historical data and identifying patterns. This empowers traders to stay ahead of the curve and make informed decisions in a dynamic market environment. Generative AI plays a crucial role in effective risk management by analyzing various factors to mitigate risks and maximize returns. Through dynamic adjustment of portfolio allocations and hedging strategies, Generative AI ensures traders can navigate volatile market conditions confidently.
 
Generative AI allows customization of trading strategies based on individual preferences and risk tolerance, tailoring investment strategies to specific goals and objectives Generative AI significantly enhances productivity in online trading and investment by swiftly analyzing vast amounts of financial data, automating routine tasks, and continuously refining strategies over time.

Overall, Generative AI represents a paradigm shift in online trading and investment, unlocking unparalleled efficiency and innovation. By harnessing AI-driven algorithms, traders can gain a competitive edge, accelerate development cycles, and achieve their financial goals with confidence in an ever-evolving market landscape.
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Generative AI Redefines Cybersecurity Defense Against Advanced Threats

 

In the ever-shifting realm of cybersecurity, the dynamic dance between defenders and attackers has reached a new echelon with the integration of artificial intelligence (AI), particularly generative AI. This technological advancement has not only armed cybercriminals with sophisticated tools but has also presented a formidable arsenal for those defending against malicious activities. 

Cyber threats have evolved into more nuanced and polished forms, as malicious actors seamlessly incorporate generative AI into their tactics. Phishing attempts now boast convincingly fluid prose devoid of errors, courtesy of AI-generated content. Furthermore, cybercriminals can instruct AI models to emulate specific personas, amplifying the authenticity of phishing emails. These targeted attacks significantly heighten the likelihood of stealing crucial login credentials and gaining access to sensitive corporate information. 

Adding to the complexity, threat actors are crafting their own malicious iterations of mainstream generative AI tools. Examples include DarkGPT, capable of delving into the Dark Web, and FraudGPT, which expedites the creation of malicious codes for devastating ransomware attacks. The simplicity and reduced barriers to entry provided by these tools only intensify the cyber threat landscape. However, amid these challenges lies a silver lining. 

Enterprises have the potential to harness the same generative AI capabilities to fortify their security postures and outpace adversaries. The key lies in effectively leveraging context. Context becomes paramount in distinguishing allies from adversaries in this digital battleground. Thoughtful deployment of generative AI can furnish security professionals with comprehensive context, facilitating a rapid and informed response to potential threats. 

For instance, when confronted with anomalous behavior, AI can swiftly retrieve pertinent information, best practices, and recommended actions from the collective intelligence of the security field. The transformative potential of generative AI extends beyond aiding decision-making; it empowers security teams to see the complete picture across multiple systems and configurations. This holistic approach, scrutinizing how different elements interact, offers an intricate understanding of the environment. 

The ability to process vast amounts of data in near real-time democratizes information for security professionals, enabling them to swiftly identify potential threats and reduce the dwell time of malicious actors from days to mere minutes. Generative AI represents a departure from traditional methods of monitoring single systems for abnormalities. By providing a comprehensive view of the technology stack and digital footprint, it helps bridge the gaps that malicious actors exploit. 

The technology not only streamlines data aggregation but also equips security professionals to analyze it efficiently, making it a potent tool in the ongoing cybersecurity battle. While the integration of AI in cybersecurity introduces new challenges, it echoes historical moments when society grappled with paradigm shifts. Drawing parallels to the introduction of automobiles in the early 1900s, where red flags served as warnings, we find ourselves at a comparable juncture with AI. 

Prudent and mindful progression is essential, akin to enhancing vehicle security features and regulations. Despite the risks, there is room for optimism. The cat-and-mouse game will persist, but with the strategic use of generative AI, defenders can not only keep pace but gain an upper hand. Just as vehicles have become integral to daily life, AI can be embraced and fortified with enhanced security measures and regulations. 

The integration of generative AI in cybersecurity is a double-edged sword. While it emboldens cybercriminals, judicious deployment empowers defenders to not only keep up but also gain an advantage. The red-flag moment is an opportunity for society to navigate the AI landscape prudently, ensuring this powerful technology becomes a force for good in the ongoing battle against cyber threats.
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Deciding Between Public and Private Large Language Models (LLMs)

 

The spotlight on large language models (LLMs) remains intense, with the debut of ChatGPT capturing global attention and sparking discussions about generative AI's potential. ChatGPT, a public LLM, has stirred excitement and concern regarding its ability to generate content or code with minimal prompts, prompting individuals and smaller businesses to contemplate its impact on their operations.

Enterprises now face a pivotal decision: whether to utilize public LLMs like ChatGPT or develop their own private models. Public LLMs, such as ChatGPT, are trained on vast amounts of publicly available data, offering impressive results across various tasks. However, reliance on internet-derived data poses risks, including inaccurate outputs or potential dissemination of sensitive information.

In contrast, private LLMs, trained on proprietary data, offer deeper insights tailored to specific enterprise needs, albeit with less breadth compared to public models. Concerns about data security loom large for enterprises, especially considering the risk of exposing sensitive information to hackers targeting LLM login credentials.

To mitigate these risks, companies like Google, Amazon, and Apple are implementing strict access controls and governance measures for public LLM usage. Moreover, the challenge of building unique intellectual property (IP) atop widely accessible public models drives many enterprises towards private LLM development.

Enterprises are increasingly exploring private LLM solutions tailored to their unique data and operational requirements. Platforms like IBM's WatsonX offer enterprise-grade tools for LLM development, empowering organizations to leverage AI engines aligned with their core data and business objectives.

As the debate between public and private LLMs continues, enterprises must weigh the benefits of leveraging existing models against the advantages of developing proprietary solutions. Those embracing private LLM development are positioning themselves to harness AI capabilities aligned with their long-term strategic goals.
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Here's How to Choose the Right AI Model for Your Requirements

 

When kicking off a new generative AI project, one of the most vital choices you'll make is selecting an ideal AI foundation model. This is not a small decision; it will have a substantial impact on the project's success. The model you choose must not only fulfil your specific requirements, but also be within your budget and align with your organisation's risk management strategies. 

To begin, you must first determine a clear goal for your AI project. Whether you want to create lifelike graphics, text, or synthetic speech, the nature of your assignment will help you choose the proper model. Consider the task's complexity as well as the level of quality you expect from the outcome. Having a specific aim in mind is the first step towards making an informed decision.

After you've defined your use case, the following step is to look into the various AI foundation models accessible. These models come in a variety of sizes and are intended to handle a wide range of tasks. Some are designed for specific uses, while others are more adaptable. It is critical to include models that have proven successful in tasks comparable to yours in your consideration list. 

Identifying correct AI model 

Choosing the proper AI foundation model is a complicated process that includes understanding your project's specific demands, comparing the capabilities of several models, and taking into account the operational context in which the model will be implemented. This guide synthesises the available reference material and incorporates extra insights to provide an organised method to choosing an AI base model. 

Identify your project targets and use cases

The first step in choosing an AI foundation model is to determine what you want to achieve with your project. Whether your goal is to generate text, graphics, or synthetic speech, the nature of your task will have a considerable impact on the type of model that is most suitable for your needs. Consider the task's complexity and the desired level of output quality. A well defined goal will serve as an indicator throughout the selecting process. 

Figure out model options 

Begin by researching the various AI foundation models available, giving special attention to models that have proven successful in jobs comparable to yours. Foundation models differ widely in size, specialisation, and versatility. Some models are meant to specialise on specific functions, while others have broader capabilities. This exploratory phase should involve a study of model documentation, such as model cards, which include critical information about the model's training data, architecture, and planned use cases. 

Conduct practical testing 

Testing the models with your specific data and operating context is critical. This stage ensures that the chosen model integrates easily with your existing systems and operations. During testing, assess the model's correctness, dependability, and processing speed. These indicators are critical for establishing the model's effectiveness in your specific use case. 

Deployment concerns 

Make the deployment technique choice that works best for your project. While on-premise implementation offers more control over security and data privacy, cloud services offer scalability and accessibility. The decision you make here will mostly depend on the type of application you're using, particularly if it handles sensitive data. In order to handle future expansion or requirements modifications, take into account the deployment option's scalability and flexibility as well. 

Employ a multi-model strategy 

For organisations with a variety of use cases, a single model may not be sufficient. In such cases, a multi-model approach can be useful. This technique enables you to combine the strengths of numerous models for different tasks, resulting in a more flexible and durable solution. 

Choosing a suitable AI foundation model is a complex process that necessitates a rigorous understanding of your project's requirements as well as a thorough examination of the various models' characteristics and performance. 

By using a structured approach, you can choose a model that not only satisfies your current needs but also positions you for future advancements in the rapidly expanding field of generative AI. This decision is about more than just solving a current issue; it is also about positioning your project for long-term success in an area that is rapidly growing and changing.
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The Dual Landscape of LLMs: Open vs. Closed Source

 

AI has emerged as a transformative force, reshaping industries, influencing decision-making processes, and fundamentally altering how we interact with the world. 

The field of natural language processing and artificial intelligence has undergone a groundbreaking shift with the introduction of Large Language Models (LLMs). Trained on extensive text data, these models showcase the capacity to generate text, respond to questions, and perform diverse tasks. 

When contemplating the incorporation of LLMs into internal AI initiatives, a pivotal choice arises regarding the selection between open-source and closed-source LLMs. Closed-source options offer structured support and polished features, ready for deployment. Conversely, open-source models bring transparency, flexibility, and collaborative development. The decision hinges on a careful consideration of these unique attributes in each category. 

The introduction of ChatGPT, OpenAI's groundbreaking chatbot last year, played a pivotal role in propelling AI to new heights, solidifying its position as a driving force behind the growth of closed-source LLMs. Unlike closed-source LLMs like ChatGPT, open-source LLMs have yet to gain traction and interest from independent researchers and business owners. 

This can be attributed to the considerable operational expenses and extensive computational demands inherent in advanced AI systems. Beyond these factors, issues related to data ownership and privacy pose additional hurdles. Moreover, the disconcerting tendency of these systems to occasionally produce misleading or inaccurate information, commonly known as 'hallucination,' introduces an extra dimension of complexity to the widespread acceptance and reliance on such technologies. 

Still, the landscape of open-source models has witnessed a significant surge in experimentation. Deviating from the conventional, developers have ingeniously crafted numerous iterations of models like Llama, progressively attaining parity with, and in some cases, outperforming closed models across specific metrics. Standout examples in this domain encompass FinGPT, BioBert, Defog SQLCoder, and Phind, each showcasing the remarkable potential that unfolds through continuous exploration and adaptation within the open-source model ecosystem.

Apart from providing a space for experimentation, other points increasingly show that open-source LLMs are going to gain the same attention closed-source LLMs are getting now.

The open-source nature allows organizations to understand, modify, and tailor the models to their specific requirements. The collaborative environment nurtured by open-source fosters innovation, enabling faster development cycles. Additionally, the avoidance of vendor lock-in and adherence to industry standards contribute to seamless integration. The security benefits derived from community scrutiny and ethical considerations further bolster the appeal of open-source LLMs, making them a strategic choice for enterprises navigating the evolving landscape of artificial intelligence.

After carefully reviewing the strategies employed by LLM experts, it is clear that open-source LLMs provide a unique space for experimentation, allowing enterprises to navigate the AI landscape with minimal financial commitment. While a transition to closed source might become worthwhile with increasing clarity, the initial exploration of open source remains essential. To optimize advantages, enterprises should tailor their LLM strategies to follow this phased approach.
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AI Poison Pill App Nightshade Received 250K Downloads in Five Days

 

Shortly after its January release, the AI copyright infringement tool Nightshade exceeded the expectations of its developers at the University of Chicago's computer science department, with 250,000 downloads. With Nightshade, artists can avert AI models from using their artwork for training purposes without acquiring permission.

The Bureau of Labour Statistics reports that more than 2.67 million artists work in the United States, but social media response indicates that downloads have taken place across the globe. According to one of the coders, cloud mirror links were established in order to prevent overloading the University of Chicago's web servers.

The project's leader, Ben Zhao, a computer science professor at the University of Chicago, told VentureBeat that "the response is simply beyond anything we imagined.” 

"Nightshade seeks to 'poison' generative AI image models by altering artworks posted to the web, or 'shading' them on a pixel level, so that they appear to a machine learning algorithm to contain entirely different content — a purse instead of a cow," the researchers explained. After training on multiple "shaded" photos taken from the web, the goal is for AI models to generate erroneous images based on human input. 

Zhao, along with colleagues Shawn Shan, Wenxin Ding, Josephine Passananti, and Heather Zheng, "developed and released the tool to 'increase the cost of training on unlicensed data, such that licencing images from their creators becomes a viable alternative,'" VentureBeat reports, citing the Nightshade project page. 

Opt-out requests, which purport to stop unauthorised scraping, are reportedly made by the AI companies themselves; however, TechCrunch notes that "those motivated by profit over privacy can easily disregard such measures." 

Zhao and his colleagues do not intend to dismantle Big AI, but they do want to make sure that tech giants pay for licenced work—a requirement that applies to any business operating in the open—or else they risk legal repercussions. According to Zhao, the fact that AI businesses have web-crawling spiders that algorithmically collect data in an often undetectable manner has basically turned into a permit to steal.

Nightshade shows that these models are vulnerable and there are ways to attack, Zhao said. He went on to say that what it implies is that there are methods for content creators to provide harder returns than writing Congress or complaining via email or social media. 

Glaze, one of the team's apps that guards against AI infringement, has reportedly been downloaded 2.2 million times since its April 2023 release, according to VentureBeat. By changing pixels, glaze makes it more difficult for AI to "learn" from an artist's distinctive style.
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Transforming the Creative Sphere With Generative AI

 

Generative AI, a trailblazing branch of artificial intelligence, is transforming the creative landscape and opening up new avenues for businesses worldwide. This article delves into how generative AI transforms creative work, including its benefits, obstacles, and tactics for incorporating this technology into your brand's workflow. 

 Power of generative AI

Generative AI uses advanced machine learning algorithms and natural language processing models to generate material and imagery that resembles human expressions. While others doubt its potential to recreate the full range of human creativity, Generative AI has indisputably transformed many parts of the creative process.

Generative AI systems, such as GPT-4, excel at producing human-like writing, making them critical for content creation in marketing and communication applications. Brands can use this technology to: 

  • Create highly personalised and persuasive content. 
  • Increase efficiency by automating the creation of repetitive material like descriptions of goods and customer communications. 
  • Provide a personalised user experience to increase user engagement and conversion rates.
  • Stand out in competitive marketplaces by creating distinctive and interesting content with AI. 

Challenges and ethical considerations 

Despite its potential, integrating Generative AI into the creative sector results in significant ethical concerns: 

Bias in AI: AI systems may unintentionally perpetuate biases in training data. Brands must actively address this issue by curating training data, reviewing AI outputs for bias, and applying fairness and bias mitigation strategies.

Transparency and Explainability: AI algorithms can be complex, making it difficult for consumers to comprehend how decisions are made. Brands should prioritise transparency by offering explicit explanations for AI-powered methods. 

Data Privacy: Generative AI is based on data, and misusing user data can result in privacy breaches. Brands must follow data protection standards, gain informed consent, and implement strong security measures. 

Future of generative AI in creativity

As Generative AI evolves, the future promises exciting potential for further transforming the creative sphere: 

Artistic Collaboration: Artists may work more closely with AI systems to create hybrid works that combine human and AI innovation. 

Personalised Art Experiences: Generative AI will provide highly personalised art experiences by dynamically altering artworks to individual preferences and feelings. 

AI in Art Education: Artificial intelligence (AI) will play an important role in art education by providing tools and resources to help students express their creativity. 

Ethical AI in Art: The art sector will place a greater emphasis on ethical AI practices, including legislation and guidelines to ensure responsible AI use.

The future of Generative AI in creativity is full of possibilities, including breaking down barriers, encouraging new forms of artistic expression, and developing a global community of artists and innovators. As this journey progresses, "Generative AI revolutionising art" will be synonymous with innovation, creativity, and endless possibilities.
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