GenAI Cybersecurity: Where are we? Threats, Risks, and More

Generative AI and it’s Cybersecurity presents both opportunities and risks. You can’t read the news at the moment without seeing a story about AI, particularly Generative AI (GenAI), which generates text, images, and other stuff. The pace at which people and organizations are adopting GenAI is remarkable. That being said, I can’t help but imagine that its pace is outstripping our ability to think about the potential risks introduced into our lives.

It would be interesting to inspect the most prominent persons and see their Generative AI Views – Elon Musk and Bill Gates.

Musk has expressed his concerns about the potential risks of Generative AI. He has been vocal about his apprehensions, rooted in the idea that technologies could eventually overpower human intelligence and become unmanageable. The latest was the Musk Calls for AI’ Regulatory Structure,’ Warns Congress of Risk.

In other contrast, Bill Gates views Generative AI as a powerful tool for innovation and creativity. He asserted that the technology could be leveraged to solve some of the world’s most pressing challenges, such as climate change, healthcare, and education.

While Generative AI made a long way from his first attempt in 1966 with Eliza, the latest stage was in November 2022 when ChatGPT was launched by OpenAI and changed the world of GenAI and how we’re considering and using GenAI. I can say for good.

This post focuses on GenAI and some of it’s security impacts. Many of the descriptions are based on the ChatGPT but can be good for other models.

The AI Model

The tech industry is racing to create highly advanced Large Language Models (LLMs) capable of executing humanlike conversations. Here below, we have mentioned a few outcomes:

  • Microsoft GPT Models (OpenAI), with the latest version of OpenAI’s GPT model, is one of the most advanced LLMs in the world. It can generate human-quality text, translate languages, write creative content, and answer your questions informally.
  • Google Bard is a large language model (LLM) developed by Google AI. It’s trained on a massive dataset of text and code and can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
  • Meta’s LLaMA is a significant language model that Meta created. It is trained on a massive dataset of text and code and can perform various tasks, including machine translation, cross-lingual natural language inference, and zero-shot transfer. LLaMA is still under development, but it can potentially be one of the most advanced LLMs in the world.

The Generative model’s performance surged with the arrival of deep learning. N-gram language modeling, an early method, generates the best sequence using learned word distribution. However, deep learning models can understand more complex relationships between words and phrases, which results in more natural and fluent text generation.

LLMs are still under development, but they have the potential to revolutionize many industries, including customer service, education, and entertainment. For example, LLMs can be used to create chatbots that can answer customer questions more naturally and informally. LLMs can also be used to create personalized educational experiences for students. LLMs can generate new forms of creative content, such as novels, poems, and scripts.

The Generative model’s performance surged with deep learning’s arrival. N-gram language modeling, an early method, generates the best sequence using learned word distribution.

The GenAI Progress

To write about GenAI progress will be an old text within a few hours. Still, let’s make some progress on this post.

GenAI has made significant progress in multiple fields, including image processing, speech recognition, and text understanding. ChatGPT is one of their most well-known AI products, and ChatGPT Plus is their latest and most advanced version.

GPT-4 is a significant improvement over GPT-3, with several advantages, including:

  • Better performance on various tasks, including text generation, translation, and code generation.
  • More humanlike and informative responses.
  • Increased ability to understand and respond to context.
  • More excellent safety and reliability.

ChatGPT Plus is still under development, but it has the potential to be a powerful tool for a variety of applications, including:

  • Customer service and support
  • Education and training
  • Content creation and marketing
  • Software development
  • Scientific research

GenAI continues to develop its GenAI technology and how it is used to improve the world around us. Here are some specific examples of how GenAI’s technology is being used today:

  • In image processing, GenAI’s technology is being used to develop new ways to detect and diagnose diseases, improve the quality of medical images, and create realistic special effects.
  • In speech recognition, GenAI’s technology is being used to develop new voice assistants, improve the accuracy of transcription software, and create new ways for people with disabilities to communicate.
  • In text understanding, GenAI’s technology is being used to develop new ways to detect fraud and misinformation, improve the accuracy of machine translation, and create new ways for people to interact with computers.

GenAI’s technology has the potential to revolutionize many industries and make our lives better in many ways. It will be exciting to see what the future holds for GenAI technology, especially for GenAI and Cybersecurity.

Experts Ponder GenAI’s Unprecedented Growth and Future.

A Tale of Attackers and Defenders

As GenAI evolves, the capabilities and potential applications of tools like ChatGPT will expand to almost every industry, application, and domain. Cybersecurity seems to be no exception to this rule. GenAI is currently changing the cybersecurity landscape by presenting significant opportunities and challenges. Specifically, modern GenAI tools empower organizations to develop adaptive defense strategies and predict potential threats through machine learning and deep learning techniques.

In a nutshell, GenAI is a powerful technology that has the potential to revolutionize Cybersecurity, both for attackers and defenders and Privacy. Below are the main perspectives from the Dark and Heroes sides.

The Dark Side: Attackers

From the attacker’s perspective, GenAI offers enhanced capabilities. It can swiftly identify and exploit system vulnerabilities, often outpacing human hackers. Moreover, its proficiency in data analysis allows it to craft highly convincing phishing emails, tailoring attacks to individual targets with unprecedented precision. Furthermore, the adaptability of GenAI-powered malware means it can adjust to its environment, rendering traditional signature-based detection methods less effective. This adaptability also extends to evasion techniques, with GenAI capable of mimicking legitimate user behavior and altering its tactics to avoid detection. The scalability of GenAI means attackers can orchestrate large-scale, coordinated attacks on multiple targets simultaneously, raising the stakes for cybersecurity professionals.

  • Auto-Exploits: GenAI is like that friend who’s always finding shortcuts. It can spot and exploit weak points in software faster than you can say, “Oops!”
  • Crafty Cons: GenAI can whip up super convincing scam emails. It’s like they’ve read your diary and know just what to say.
  • Shape-shifting Malware: Think of malware that changes its look every time you spot it. That’s GenAI for you.
  • Blending In: GenAI can act like a normal user, making it super tricky to spot when it’s up to no good.
  • Always One Step Ahead: If you block its path, it finds another. It’s like playing whack-a-mole with a ghost.
  • All at Once: Imagine a thousand hackers attacking at once. That’s GenAI on a caffeine rush.

The Heroes: Defenders

On the defense side, GenAI is a potent ally. It can analyze vast datasets to predict potential attack vectors, enabling proactive defense measures. This predictive capability is complemented by its ability to identify software vulnerabilities and deploy patches autonomously. Rather than relying on known signatures, GenAI emphasizes behavioral analysis, making it adept at detecting anomalies and zero-day attacks. Its real-time response capabilities can mitigate threats before they escalate, and its continuous learning from the global threat landscape ensures it remains updated. Moreover, GenAI optimizes the allocation of cybersecurity resources, ensuring that critical assets receive the highest protection.

  • Crystal Ball Analysis: GenAI can predict attacks before they happen. It’s like having a weather forecast for cyber threats.
  • Auto-Fixes: Found a weak spot? GenAI patches it up before you even knew it was there.
  • Sherlock Mode: Instead of looking for obvious clues, GenAI spots the odd ones out, catching sneaky attacks.
  • Instant Reflexes: Spot a threat? GenAI’s on it in a flash.
  • Brain Food: GenAI munches on global threat data, getting smarter every day.
  • Training Montages: It practices by simulating attacks, so it’s always ready for the real deal.
  • Best Bang for Your Buck: GenAI ensures the most important stuff gets the strongest shields.


However, the intersection of GenAI and Privacy is where things become particularly intricate. With its unparalleled data processing capabilities, GenAI can analyze and correlate vast amounts of personal data, potentially leading to invasive profiling of individuals. While this can be beneficial for personalized services, it poses significant risks if misused. Users may find their behaviors, preferences, and even their most private moments analyzed and potentially exploited. The ethical implications of such deep data analysis are profound, and there’s a pressing need for robust privacy regulations and frameworks to ensure that GenAI is used responsibly.

Developing ethical guidelines for using GenAI to protect people’s Privacy is essential. We also need to create new technologies to detect and prevent the misuse of GenAI.

Need to Know – In addition to the benefits and risks mentioned above, GenAI also has the potential to change the way we think about Cybersecurity altogether. For example, GenAI could be used to develop new security paradigms that are more proactive and predictive. For instance, GenAI could be used to create security systems that can learn from historical data to identify new and emerging threats or to build security systems that predict future attacks based on attackers’ behavior.

Offensive, Defensive, and Privacy

Let’s take the attackers, defenders, and Privacy one step ahead and see how it becomes powerful on each side, where the Offensive, like in other areas, is still winning in many places. So, what could be the risks, threats, opportunities, and others in GenAI? It depends on many principles. Below is an additional view from Offensive, Defensive, and Privacy.

The Offensive Side

There are many attack scenarios in GenAI. Below are the most prominent:

Attack Payloads are portions of malicious code that execute unauthorized actions, such as deleting files, harvesting data, or launching further attacks. An attacker could leverage ChatGPT’s text generation capabilities to create attack payloads. Consider a scenario where an attacker targets a server running a database management system that is susceptible to SQL injection. The attacker could train ChatGPT on SQL syntax and techniques commonly used in injection attacks and then provide it with specific details of the target system. Subsequently, ChatGPT could be utilized to generate an SQL payload for injection into the vulnerable system.

Automated Hacking – LLMs can generate code that can be used for automated hacking attacks and scanning software code for vulnerabilities. For example, Malware Creation: Malicious actors could potentially use LLMs to generate new and advanced malware strains or to optimize existing malware to be more effective.

Payload Generation – Payload is a command that initiates unauthorized tasks, such as harvesting information, deleting data, opening doors, etc. Chat GPT has proven to be a game-changer in this domain as well. Whether I need a custom payload for a specific exploit technique or want to explore different encoding and obfuscation methods, Chat GPT has got me covered.

Code Creation Made Effortless – One of the challenges I often face during penetration testing is the need to quickly develop custom code snippets or scripts to perform specific tasks. Chat GPT has proven to be an invaluable companion in this regard. By conversing with the model, I can describe the required functionality, and it generates relevant code snippets in response. This saves me precious time and effort, allowing me to focus on other critical aspects of my testing process.

Social Engineering – An attack can be carefully disguised as a message coming from a legitimate entity, prompting a victim to perform specific actions. For example, ChatGPT has become a  tool for Cybercriminals in the Social Engineering areas.

Note: ChatGPT is described here as the example but the same example can by in any other platforms.

Prompt injection and GenAI risks

The SWITCH method is a bit like a Jekyll-and-Hyde approach, where you instruct ChatGPT to alter its behavior dramatically. The technique’s foundation rests upon the AI model’s ability to simulate diverse personas, but here, you’re asking it to act opposite to its initial responses

Reverse psychology is a psychological tactic involving the advocacy of a belief or behavior contrary to the one desired, with the expectation that this approach will encourage the subject of the persuasion to do what is desired. Applying reverse psychology in our interaction with ChatGPT can often be a valuable strategy to bypass certain conversational roadblocks.

Phishing Attacks are a prevalent form of cybercrime wherein attackers pose as trustworthy entities to extract sensitive information from unsuspecting victims. Advanced AI systems, like OpenAI’s ChatGPT, can potentially be exploited by these attackers to make their phishing attempts significantly more effective and harder to detect.

The Defensive Side

With advancing technology, enterprises will witness emerging cybersecurity defense use cases for ChatGPT. Incorporating diverse technical, organizational, and procedural controls ensures effective measures.

Here are some of the ways that ChatGPT can be used to improve Cybersecurity:

Threat Hunting can help organizations hunt for threats on their networks. For example, it can analyze security logs and alerts for anomalous activity and identify potential hazards that may have evaded traditional security controls.

Risk Assessment and Management can help organizations assess and manage their cybersecurity risks. For example, it can identify and prioritize vulnerabilities and develop and implement risk mitigation strategies.

From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

Identification of cyber attacks can be used to identify cyber-attacks in real-time. This can help organizations minimize the damage caused by attacks and respond quickly to mitigate the risks.

Incident Response Guidance can be used to provide guidance to security teams during incident response. This can help couples to respond to incidents more quickly and effectively.

Threat intelligence can be used to collect, analyze, and share threat intelligence. This can help organizations avoid the latest threats and develop effective countermeasures.

Malware Detection can be used to develop new and innovative malware detection techniques. This can help to identify and stop new and emerging malware threats.

Automation can automate many cybersecurity tasks, such as analyzing security logs and alerts, patching vulnerabilities, and configuring security devices and systems. This can free up security teams to focus on more complex tasks and improve security operations’ overall efficiency and effectiveness.

Secure code generation and detection can generate secure code and detect security vulnerabilities. This can help to improve the security of software applications and systems.

Security Research can generate new ideas and hypotheses about cyber threats. This can help security researchers to develop new and innovative ways to protect organizations from cyber attacks.

Reporting can be used to generate comprehensive and insightful cybersecurity reports. This can help organizations better understand their security posture, identify trends and patterns, and make informed decisions about improving their security.

Secure Access (Zero-Trust) can be used to help organizations implement a secure access architecture. For example, ChatGPT or Google Bard can generate and manage dynamic access control policies and monitor and analyze user behavior for anomalous activity.

Recommended defensive tools and techniques for each phase of the Cyber Kill Chain (CKC) framework that organizations should adopt in conjunction with GenAI-enabled security strategies for comprehensive cyber defense.

From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

The Privacy, Social, and Other Sides

ChatGPT is a powerful new language model with the potential to revolutionize many industries and aspects of our lives. However, it is essential to be aware of the potential social, legal, and ethical implications of its use.

One of the biggest concerns is the pervasive role that ChatGPT could play in our society. As it becomes more widely adopted, it will likely be used in various applications, from customer service to education and entertainment. This raises questions about how we will interact with and rely on ChatGPT and ensure it is used responsibly and ethically.

Another concern is the controversy over data ownership and rights. ChatGPT is trained on a massive dataset of text and code, much of which is protected by copyright. This raises questions about who owns and has rights to the data that ChatGPT uses and how this data is used and protected.

There are also concerns about unauthorized access to user conversations and data breaches. ChatGPT is often used to collect and store user conversations and data. This raises concerns about the security of this data and the potential for unauthorized access.

ChatGPT can also be misused by organizations and employees in various ways, such as to create deepfakes, spread misinformation, or engage in cyberbullying. It is essential to have safeguards to prevent these types of misuse. Additionally, ChatGPT can misuse personal information, such as tracking users without their consent or creating targeted advertising campaigns. It is essential to have strong privacy protections to protect users from this type of misuse.

ChatGPT is sometimes known to generate factually incorrect or misleading text. This is known as “hallucination.” It is essential to be aware of this limitation of ChatGPT and verify the accuracy of any information it generates.

These are just some of the potential social, legal, and ethical implications of ChatGPT. As ChatGPT develops and becomes more widely used, new implications will likely emerge. It is essential to be aware of these implications and to take steps to mitigate them. One way to mitigate these risks is to develop clear ethical guidelines for using ChatGPT.

These guidelines should address data ownership, Privacy, and misuse. It is also essential to develop tools and techniques for detecting and preventing the abuse of ChatGPT.

Ultimately, it is up to all of us to ensure that ChatGPT is used responsibly and ethically. By being aware of the potential risks and taking steps to mitigate them, we can help to ensure that ChatGPT is a force for good in the world.

Cyber Kill Chain and SRM

Like any other topics, subjects, and model in Cybersecurity, for GenAI, it will be the same with the required adaptation: The Cyber Kill Chain (CKC) and the Shared Responsibility Model (SRM).


In Cybersecurity, this principle holds true where risks abound. The ever-increasing dangers and threats in the digital landscape necessitate a comprehensive approach to defense. To address the problem of evolving risks, the Cyber Kill Chain (CKC) concept was developed. Inspired by Lockheed Martin’s military model, CKC is an intelligence-driven framework for Cybersecurity.

Tracing an attack utilizing the Cyber Kill Chain (CKC) attack framework. In the first stage, the threat actor gathers intelligence through reconnaissance. The second stage is weaponization, where threat actors create attack vectors (e.g., malware) based on identified vulnerabilities. The third stage is delivering a malicious payload in the target environment. A malicious payload is triggered in the fourth stage to exploit vulnerabilities. In the fifth stage, backdoors are installed to maintain undetected persistence. The sixth stage is about establishing command & control in the target environment for remote communication. Threat actors achieve their goals, such as financial gain, data exfiltration, geopolitical gain, etc., after a successful attack in the final stage.

From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

Shared Responsibility Model

Generative AI is a powerful new technology with the potential to transform many industries and aspects of our lives. However, with great power comes great responsibility. Using generative AI responsibly and ethically is essential to minimize the risk of negative consequences.

The generative AI shared responsibility model is a framework for defining and allocating responsibilities for security and compliance between the different actors involved in developing and using productive AI applications. The model is based on the principle that all parties involved have a role in ensuring that generative AI is used responsibly and ethically.

The three main actors involved in the generative AI shared responsibility model are:

  • AI Service Provider (AISP): The AISP is the entity that provides AI services on demand to users to help them build AI applications. These entities could be cloud providers like AWS, GCP, Azure, and other specialized providers.
  • AI Service User (AISU): The AISU is the entity that uses services provided by the AISPs to build AI applications for users. These entities are generally enterprises, SMBs, startups, or individual developers.
  • Enterprise (Application Owner): The Enterprise is the entity that owns and operates the generative AI application. This could be the same as the AISU, or it could be a separate entity.

The following table summarizes the key responsibilities of each actor in the generative AI shared responsibility model:

  • Provide secure and compliant AI services.
  • Ensure that AI models are trained on high-quality data free from bias and discrimination.
  • Develop and implement mechanisms to prevent the misuse of AI models.
  • Design and implement secure and compliant AI applications.
  • Manage user access and permissions.
  • Monitor and respond to security threats.
  • Evaluate the ethical implications of the AI application before deploying it.
  • Take steps to mitigate any potential negative impacts of the AI application.
  • Oversee the development and operation of the generative AI application.
  • Ensure that the application is used responsibly and ethically.
  • Comply with all applicable laws and regulations.
  • Establish policies and procedures for the responsible use of generative AI.
  • Train employees on the ethical and responsible use of generative AI.
  • Implement safeguards to protect user data and Privacy.
  • Monitor and respond to security threats.

The generative AI shared responsibility model is essential to ensure the safe and responsible development and use of generative AI. By clearly defining the roles and responsibilities of each actor involved, the model helps to reduce the risk of security breaches, privacy violations, and other negative consequences.

In addition to the above, here are some other professional considerations for implementing the generative AI shared responsibility model:

  • Transparency: All parties involved should be transparent about their roles and responsibilities and the data and algorithms being used.
  • Accountability: All parties involved should be accountable for their actions and the outcomes of using generative AI.
  • Collaboration: All parties involved should collaborate to develop and implement best practices for the responsible use of generative AI.

The generative AI shared responsibility model is a new and evolving concept. It is essential for all parties involved to work together to ensure its effective implementation. By doing so, we can help to ensure that generative AI is used to benefit society and not to harm it.

From Cloud Security Alliance

Promising Solutions and Frameworks

Gen AI is a rapidly evolving field with the potential to revolutionize how we secure our systems and data. Gen AI-based security solutions can help organizations detect and respond to threats more quickly and effectively, automate security tasks, and improve their overall security posture.

Several promising frameworks have been developed for developing and deploying Gen AI-based security solutions. Some of the most notable frameworks include:

TensorFlow Security is a framework from Google AI that provides tools and libraries for developing and deploying AI-based security solutions. It includes a variety of pre-trained models that can be used for tasks such as threat detection, anomaly detection, and fraud prevention.

PyTorch Lightning is a framework that provides a high-performance and flexible way to train and deploy AI models. It is well-suited for developing Gen AI-based security solutions, as it can be used to train and deploy complex models on various hardware platforms.

ONNX Runtime is an open-source inference engine that can deploy AI models trained with various frameworks, including TensorFlow, PyTorch, and MXNet. It is well-suited for deploying Gen AI-based security solutions to production environments, as it is optimized for performance and scalability.

In addition to these general-purpose frameworks, a number of specialized frameworks have been developed for developing Gen AI-based security solutions. For example, the Microsoft Security Copilot platform includes several AI-powered features for detecting and responding to cyberattacks. The Google Cloud Security AI Workbench also consists of several AI-powered tools for automating and streamlining security operations.

These frameworks can be used to develop Gen AI-based security solutions for various applications. For example, Gen AI-based security solutions can be used to:

Detect and respond to cyberattacks: Gen AI-based security solutions can monitor network traffic, analyze behavior patterns, and detect malicious activities in real-time. This can help organizations to detect and respond to cyberattacks more quickly and effectively.

Automate security tasks: Gen AI-based security solutions can automate various tasks, such as threat detection, incident investigation, and risk management. This can free up security analysts to focus on more strategic initiatives.

Improve security posture: Gen AI-based security solutions can improve an organization’s overall security posture by identifying and mitigating vulnerabilities and providing insights into security risks and threats.

The adoption of Gen AI-based security solutions is still in its early stages, but the potential benefits of these solutions are significant. As the field of Gen AI continues to evolve, we can expect to see even more innovative and effective security solutions emerge.

Here are some specific examples of how Gen AI-based security solutions are being used today:

  • Microsoft Security Copilot is used by organizations such as JPMorgan Chase and Coca-Cola to detect and respond to malware attacks, phishing attacks, and other cyber threats.
  • Google Cloud Security AI Workbench is being used by organizations such as Lowe’s and Target to automate tasks such as security incident investigation and threat hunting.
  • CrowdStrike Charlotte AI is used by organizations such as Nasdaq and Netflix to protect their workloads from sophisticated cyberattacks, such as ransomware and zero-day attacks.

These are just a few examples of how Gen AI-based security solutions are being used today. As the field of Gen AI continues to evolve, we can expect to see even more innovative and effective security solutions emerge.

Gen AI-based security solutions have the potential to revolutionize the way we secure our systems and data. By leveraging the power of AI, these solutions can help organizations detect and respond to threats more quickly and effectively, automate security tasks, and improve their overall security posture.

Microsoft Security Copilot

Microsoft Security Copilot is a separate offering from the products that currently exist in the Microsoft Security portfolio. There is no word yet, however, on whether or not Security Copilot will help someone navigate the complexities of Microsoft enterprise licensing. Early phases of the offering:

    1. Give human-readable explanations of vulnerabilities, threats, and alerts from first- and third-party tools.
    2. Answer human-readable questions about the enterprise environment.
    3. Surface recommendations on the next steps in the incident analysis.
    4. Allow security pros to automate those steps. For example, it can be used to gather information on the environment, generate visualizations of an attack, execute a response, or, in certain cases, even be used to reverse-engineer malware.
    5. Generate PowerPoint documents based on an incident investigation.

Security Copilot is poised to become the connective tissue for all Microsoft security products. Importantly, it will integrate with third-party products, as well. This is a hard and fast requirement for an assistant that can provide comprehensive and consistent value.

Google’s Secure AI Framework

SAIF is inspired by the security best practices — like reviewing, testing, and controlling the supply chain — that we’ve applied to software development while incorporating our understanding of security mega-trends and risks specific to AI systems.

A framework across the public and private sectors is essential for making sure that responsible actors safeguard the technology that supports AI advancements so that when AI models are implemented, they’re secure by default. Today marks an important first step.

Need to Know – Gen AI security tools are on the rise and have many solutions and frameworks, From RadiantSecurityCrowdStrike Charlotte AI, and many more.


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