Intro to Gen AI

In the last three years, it’s as if AI has become a household name. Though the term has been around since the 1950s, OpenAI’s release of ChatGPT in 2022 boosted its popularity and led to widespread adoption and innovation in the field, and it is showing no signs of slowing down. AI and cybersecurity are now more connected than ever, with AI playing a key role in digital defense strategies. NVIDIA has laid out its outlook for the following stages of artificial intelligence, moving from perception to generative to agentic to physical.

Though enterprises’ primary focus has now shifted to agentic AI and implementing agentic workflows in their businesses, Generative AI still plays a huge role and is often the form factor of AI that people interact with the most on a day-to-day basis. Generative AI is AI that can generate new content in various forms (text, images, videos, audio, etc) by training on large datasets.

Using neural networks, it identifies patterns and structures within said datasets, using that information to understand users’ natural language requests and generate new and original content.

Gen AI’s Impact on Security

We have seen how AI has transformed everything around us, so one can only imagine how much it has transformed cybersecurity as an industry. The use of AI in cyber security has expanded rapidly, transforming how enterprises detect, respond to, and prevent cyberattacks. Generative AI cyber security, in particular, can simulate cyberattacks and produce faux datasets, allowing the AI to evolve and adapt to new threats as they emerge.

Through training, it can better understand the nuances of security data and identify patterns indicative of cyber threats like malware, ransomware, and unusual network traffic that traditional detection systems may miss. By learning from historical data, we can establish a baseline of standard activity, allowing for flagging any deviations that may indicate an incident.

While Generative AI has enabled security practitioners to do their jobs more effectively, it has simultaneously created many risks on top of what were already threats in cybersecurity, from allowing cybercriminals to carry out more creative and effective attacks to making misinformation more prominent. Let’s dive deeper into some of these security risks.

Security Risks with Gen AI

1. Phishing and Social Engineering
   Criminals can use Generative AI to generate personalized content that mimics legitimate communication, tricking people into sharing sensitive information or even downloading malware. This has become one of the most pressing AI cybersecurity threats for enterprises. While phishing has been around since the dawn of the internet, AI has allowed it to become increasingly sophisticated, able to mimic writing styles and automate attacks at scale. Often, it is hard to distinguish from actual conversations, meaning there is a higher risk of successful scams. Including personas of actual people with personal information is possible, allowing for more convincing impersonation. AI trained on extensive social network data can target attacks at each addressee, improving phishing effectiveness and often evading legacy systems.
   
   
2. Deepfake Generation
   Deepfakes are hyperrealistic images, audio, or videos created using generative AI that bad actors can use to impersonate people. It has become a pressing concern as it enables fake news by creating very realistic footage that can sway public opinion. Cybersecurity with AI tools is now being used to detect deepfake media at scale. In a time of such high polarity and political tension, this is far beyond entertainment and pranks, as it can lead to identity theft of very high-level people, such as politicians, C-suite executives, and celebrities, leading to reputation damage, political instability, and financial fraud, among others. They have become a critical tool for misinformation fueled by Generative AI.
   
   
3. Malware & Malicious Code Generation
   Generative AI software development tools, such as GitHub Copilot, Lovable, Windsurf, and Cursor, have become increasingly popular recently. Though they are loved by builders worldwide and can be very useful in creating solutions, these tools also enable attackers to produce new and malicious code far more easily. Malware can be designed to adapt and evolve its identifiable features to avoid detection by antivirus and malware detection tools.
   
   
4. Adversarial Attacks on AI Systems
   Attackers leverage Gen AI’s multimodal capabilities to create a variety of manipulated inputs, such as slightly altered images, audio, and text, to trick an AI model into making wrong decisions. These changes are often undetectable by humans but confuse the AI, leading to misleading outputs.
   
   For example, AI could create an image that appears normal to us but results in image recognition software misidentifying it. It could also generate text that surpasses spam filters and content moderation tools. These adversarial attacks undermine the reliability of AI security tools, creating blind spots where threats can quietly slip through. AI based cyber security teams are focusing on building more robust models to defend against these adversarial attacks.
   
   
5. Training Data Leakage & Privacy Violations
   Training data makes Gen AI possible, as patterns and structures are identified in the datasets to produce user outputs. What happens if sensitive training data is unintentionally exposed? If a model is trained poorly, it might unknowingly include trade secrets in its output. If an AI model memorizes and regenerates private information, this leads to breaches of confidentiality. As data gets more complex, the risk of leakage increases and can happen in subtle ways that are hard to detect. Cybersecurity for AI is now focusing on preventing such data leakage risks at every stage of the AI lifecycle.
   
   
6. Model Poisoning
   Knowing that these models rely entirely on their training data, attackers sometimes target this data and alter or poison it. This can also work by injecting malicious data points into a training set, which can cause models to fail or function unpredictably.
   
   For example, in code generation models, it could propose code with vulnerabilities, making it easier to penetrate. Model poisoning becomes especially dangerous in fields like autonomous driving or the financial sector, as the consequences are dire. It undermines the trustworthiness of AI applications. AI network security tools now include model integrity monitoring to prevent these types of attacks.
   
   
7. Automated Vulnerability Discovery & Exploitation
   All systems and software have vulnerabilities, which bad actors aim to find and act on. Gen AI has allowed attackers to analyze individuals, systems, and software more easily and efficiently for weak points to launch more targeted attacks. As these models become more sophisticated, they become targets for theft, and those with access can use them to find and exploit vulnerabilities. The latest developments in cybersecurity AI are focused on rapidly identifying and patching these discovered vulnerabilities.
   
   

Mitigations to These Risks

Given the number of risks that Gen AI has brought to security, it is crucial to discuss how to combat them.

1. Building a Strong AI Governance and Oversight Framework
   One of the best things an enterprise can do to get the best out of AI while still maintaining security is to develop an AI Governance Framework. It is vital to have clear guidelines for AI development and deployment. Defining roles and responsibilities for those involved with AI projects, having oversight mechanisms to ensure compliance, and setting protocols for risk assessment help ensure that employees are using AI responsibly. Also, keeping detailed records of training data, model development, and deployment is incredibly helpful to look back on.
   
   
2. Secure Generative AI with Data Classification, Anonymization, and Encryption
   Data is the new gold, and managing data carefully in the AI age is essential. Classifying data allows for its appropriate safeguarding, depending on the sensitivity of the information. Anonymizing data removes any personally identifiable information, which protects privacy and reduces the impact of a leak. Encryption makes data unreadable to unauthorized users, providing a strong layer of security, and is extremely important both at rest and in transit. Maintaining regular data audits and having proper data retention policies helps prevent leaks. Many AI cybersecurity companies now specialize in providing these data protection services.
   
   
3. Invest in Employee Readiness for AI Security and Ethics
   Using AI for cybersecurity awareness training is becoming a core part of many companies’ security programs. Employees should be competent in Gen AI, security, and AI ethics risks. Training employees is of the utmost importance, as an informed workforce will know how to identify and mitigate risks when they arise. Establishing clear internal AI usage policies and keeping employees informed of these ground rules ensures interactions are consistent with the organization’s security protocols. AI in cyber security courses are now being rolled out in many organizations to help employees stay updated.
   
   
4. Protect Work Data from Misuse in Generative AI
   Organizations must establish strict guidelines and control mechanisms to protect against the risk of training data leakage and ensure the security of sensitive world data. There needs to be clear boundaries on what data types can be used for training and operations. For example, security practitioners should prohibit specific categories of information within an enterprise from being input into AI systems, such as personal employee info. Access controls should be implemented based on roles so that only authorized personnel can access sensitive data and these AI systems. It is essential to continuously monitor and audit the use of data within AI systems. AI powered cybersecurity tools with role-based access control (RBAC) are now used to enforce these guidelines.
   
   
5. Invest in Cybersecurity Tools Built for AI Threats
   Given the plethora of risks that Generative AI has brought to security, an increasing number of tools have emerged to address these risks. Investing in advanced cybersecurity tools is essential for defending against these threats. Enterprises should look for systems capable of detecting AI-generated anomalies and AI-specific vulnerabilities. Tools should be able to monitor AI usage within the company to prevent employees from uploading confidential information like client data or source code. Though it may seem harmless, it is important to consider that these platforms may retain and use the data for future training. Logging AI-related activities, flagging risky behavior, and blocking unauthorized access is vital. There is also potential for GenAI to help with security efforts, as it can identify emails created by GenAI. More companies are adopting AI cybersecurity solutions tailored to these modern threats.
   
   

How Anzenna Can Help

Anzenna is the perfect tool to help mitigate the risks of GenAI and provides many of the mitigations suggested to combat the security threats listed. As one of the cutting-edge cybersecurity tools addressing AI risks, it serves as a great addition to any enterprise looking to protect its company’s IP better. Among AI cybersecurity companies, Anzenna stands out for its proactive defense features. Anzenna monitors employee activities and flags any concerning behavior, as well as any risky Gen AI behavior. It keeps track of what is being uploaded to these AI systems and can stop the process if any information is critical.

Anzenna also provides strict access controls and authentication, allowing enterprises to control which roles have access to what information or actions. If any activity is flagged as risky, security practitioners can push training videos to those specific employees, resulting in a more informed workforce.

Overall, Anzenna ticks most of the boxes necessary to protect enterprises against the risks of Generative AI while still allowing them to leverage it for its benefits.