05-Jan-2024
Artificial intelligence (AI) is not a new concept, but with the public release of ChatGPT and the rise of generative AI models, it has gained new and dizzying momentum. AI is rapidly altering the field of cybersecurity and evolving the threat landscape. With over 100 zettabytes of data estimated to be available on the internet and a lack of comprehensive legislation in place to regulate Generative AI‘s access to it, both individuals and businesses are vulnerable to risks such as personal data privacy violations, intellectual property rights infringement, algorithm biases, and more. Furthermore, hackers are now utilizing the same AI for altering data, which is the most common cause of fraud.
Hackers can now employ generative AI’s code-writing capabilities to build malicious code, allowing them to exploit software weaknesses. The skill required to launch a ransomware assault is today as simple as creating a malicious prompt and gaining access to ransomware-as-a-service tools. This has resulted in a completely new round of attacks of an unparalleled magnitude.
Algorithm bias and Data privacy manipulation
Large amounts of data are used to train generative AI models. If the data is skewed, the generative AI model will be skewed as well. This indicates that the generative AI model may generate discrimination or unjust content.
For example, if a generative AI model is trained on a dataset of overwhelmingly favorable product evaluations, the generative AI model will be more likely to generate positive ratings. As a result, consumers may be misled regarding the quality of products.
Data privacy can also be manipulated using generative AI models. A generative AI model, for example, might be used to generate phony photos or recordings of individuals. This may be put to use to defame or harass people.
The risks of Algorithm bias and Data privacy manipulation in generative AI
There are a number of things that can be done to mitigate the risks of algorithm bias and data privacy manipulation in generative AI, including:
● Using varied and representative datasets:
Generative AI models should be trained on datasets that are both diverse and representative of the population. This will help to lessen the likelihood of the model being biased.
● Auditing generative AI models for bias:
Generative AI models should be audited for bias on a regular basis. This will aid in identifying and correcting any biases in the model.
● Educating the public about the hazards of generative AI:
The public has to be educated about the risks of generative AI, including the risks of algorithm bias and data privacy manipulation. This will encourage people to be more cautious of the content they consume and to take efforts to protect their privacy.
The Beneficial Effects of Generative AI on Cybersecurity
Generative AI plays a multidimensional role in cybersecurity, altering existing protection mechanisms and boosting the capabilities of cybersecurity specialists. Let us look at some of the major roles of Generative AI in cybersecurity:
● Provide realistic training data:
Generative AI can be used to provide realistic training data for machine learning models that can be used to detect and prevent attacks. This is significant since machine learning models require a vast amount of data to be trained on in order to be effective. By generating realistic examples of phishing emails, bogus websites, and dangerous code, generative AI can assist in the creation of this data.
● Create new security tools:
Generative AI can be used to create new security tools for detecting and preventing assaults. Generative AI, for example, can be used to create tools that generate realistic phishing emails and bogus websites. These technologies can be used by security analysts to discover and assess potential attacks or to train machine learning models.
● Improve the security of current systems:
Traditional cybersecurity methods can occasionally suffer from latency concerns, resulting in delays in detecting and responding to cyber threats. The real-time threat detection capabilities of Generative AI enable rapid response times, lowering potential damages and mitigating the impact of cyber attacks.
Traditional cybersecurity frequently takes a reactive strategy, responding to events after they occur. Generative AI enables firms to take a proactive approach, predicting and reducing possible hazards before they occur.
● Automating Cyber security chores:
Cybersecurity experts are overburdened with mundane chores that redirect their focus away from more pressing challenges. Automating routine processes like log analysis, threat hunting, and incident response with generative AI frees up human experts to focus on strategic and challenging challenges.
Synthetic data can be generated using generative AI, which can then be used to enhance real data. This can be used to train machine learning models or to test the efficacy of security solutions. Synthetic data that is comparable to real data but does not contain sensitive information can be manufactured.
A two-edged sword
Of course, generative AI can be employed as a tool by cyber attackers, providing them the same powers as defenders. Less experienced attackers, for example, can use it to craft more appealing emails or more convincing deep fake videos, audio, and photos to deliver to phishing targets. Bad actors can also use generative AI to easily recreate a known attack code to be just different enough to avoid detection.
For malicious attackers, generative AI has undoubtedly become a hot topic. In 2023, mentions of generative AI on the dark web increased. It’s common to see hackers bragging about using ChatGPT. One hacker claimed that he was able to generate malware strains from academic journals using generative AI, such as a Python-based stealer that can seek and recover popular file types (.docx, PDF, pictures) across a system.
