The threat landscape in cybersecurity and data protection is evolving, and startups now face new challenges as AI technology rapidly advances and cyber attackers look to employ new tactics. From realistic phishing emails to deepfaked videos, malicious actors are leveraging AI to craft highly convincing and targeted attacks with greater precision and scale.
For startups and SMEs, it is essential that they are well prepared for these challenges and are aware of some of the major threats, both in the present and short-term future. Let’s take a look at three ways in which cybercrime is set to evolve over the next five years.
Commodification of malicious services
The emergence of new tactics such as Jailbreak-as-a-Service highlights the democratisation of cyber threats, underscoring the need for startups to stay ahead of the curve. Recent technological developments mean that hostilities can now come from anywhere, which makes threat detection increasingly complex, particularly as we look ahead to the future.
Ransomware rendered by Malware-as-a-service is becoming more frequent – this is where a ransomware group or gang sells its ransomware code or malware to other hackers, who then use it to carry out their own ransomware attacks. This sort of activity makes it considerably easier for less sophisticated actors to attack businesses independently.
With the commodification of deepfake services, cybercriminals can easily bypass security measures, leading to devastating consequences such as financial loss and reputational damage. The now infamous case of the Hong Kong finance worker who transferred US$25 million to a fake CFO serves as a cautionary tale to organisations to be more vigilant in the new landscape.
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High-profile individuals tend to be more prone to spoofing, while government and financial institutions are more attractive targets of ransomware for the amount of sensitive information they hold.
AI model theft and misuse
The potential theft or unauthorised access to AI models developed by startups is another rising issue that will continue to proliferate as more tech startups come onto the scene with proprietary AI solutions over the next couple of years. Stolen models could be misused for malicious purposes or replicated by competitors, leading to intellectual property theft.
Techniques like model extraction attacks pose such a risk, where an adversary prompts the chatbot to divulge information that allows it to recreate its model. Model inversion techniques that enable output data to be used to reconstruct sensitive input data are also gaining ground.
The data is then exposed to further misuse. Adversarial attacks are another form of model misuse that is increasing in prominence, too – they aim to manipulate the model’s inputs to generate incorrect outputs, undermining its reliability and integrity.
AI supply chain attacks
The complex AI supply chain, involving data sourcing, model training, and deployment, presents multiple attack vectors that startups must secure. But this doesn’t only apply to AI software in general, which is experiencing more supply chain attacks.
Bad actors increasingly see software, developer infrastructure and third-party providers as entry vectors into governments and corporations. This is a threat that will only proliferate as organisations continue to integrate AI into their infrastructures.
Cybercriminals will look to attack AI supply chains via a variety of methods, including data poisoning attacks, which involve injecting malicious data into training datasets to compromise the model’s performance and introduce vulnerabilities. Meanwhile, model skewing attacks manipulate the training process to introduce targeted biases, backdoors, or other vulnerabilities into the AI model.
The expanded requirements and capabilities of data protection and data governance
To mitigate these risks, startups should implement robust data governance practices, invest in explainable AI technologies, as well as conduct regular audits for bias and fairness to maintain human oversight in critical decision-making processes involving AI. Collaboration with security experts and staying updated on the latest AI threats and best practices is also essential.
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Organisations must adopt a comprehensive approach to data governance, especially when adopting AI to process personal information. In practice, this requires a layered strategy accompanied by accountable data handling practices by humans. With many tech startups seeking to roll out their own AI solutions, adopting a privacy-by-design approach where privacy considerations are integrated into every stage of the development and operation of a system is expected.
Proactive threat intelligence, conducting robust third-party due diligence, and implementing stringent data protection and encryption protocols are valuable precautions. Startups that collect, use, process or disclose personal data with Gen AI, may consider adopting Privacy Enhancing Technologies (PETs) that enable data analysis without compromising personal information, such as differential privacy, federated learning, and homomorphic encryption.
As many technological controls one can take, human error is still one of the key causes of high-profile data breaches, especially in rapidly growing startups. As such, educating employees and executives on data handling best practices and the risks of AI is crucial.
Educated leaders and staff training are essential in the age of AI
Effective data handling and security, especially when leveraging AI as part of your product or services, hinges on a well-informed workforce that is not limited to IT professionals. Staff training programmes tailored to individual roles and responsibilities should be carried out, including realistic simulations that provide practical experience in handling cyber threats.
There are also internationally recognised courses that integrate data governance, generative AI and privacy security, suited for CTOs and IT personnel in tech startups looking to forward their venture. The International Association of Privacy Professionals’ (IAPP) Certified Privacy Information Technologist (CIPT) course imparts techniques to manage cybersecurity risks while enabling prudent data use for business purposes.
Continuous vigilance and accountability are paramount to strengthening organisational resilience against social engineering tactics and human error-induced breaches. With the right knowledge and training, leaders and employees will be better equipped to successfully implement best practices and processes early in your startup’s stage of growth so that you can sustainably adapt to evolving regulations and business growth.
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