Shahid Hanif is the Chief Technology Officer and Co-Founder of ShuftiPro, a biometric identity verification (IDV) solution.
The global technology ecosystem has a massive demand for custom software solutions.
From individuals to successful businesses, everyone is looking for personalized solutions that can meet their needs with minimal investment of resources. In fact, according to Grand View Research, the custom software development market was valued at $29.29 billion in 2022 and is expected to grow at a CAGR of 22.4% from 2023 to 2030.
This change is mainly due to rapid innovation in the technology industry. To remain competitive, businesses must adapt to changing market dynamics and emerging technologies like artificial intelligence (AI) and blockchain to differentiate themselves from competitors.
Although these recent technological advances have encouraged growth, they have also given rise to a new set of challenges. It is important to note that those developing these technologies may not be fully aware of the biggest challenges. Gartner reported that security and privacy were not considered the main barriers to developing AI technologies, but that “41% of organizations reported having previously experienced a known AI privacy breach or security incident.
In this article, I’ll take an in-depth look at some specific challenges that businesses need to be aware of when integrating AI and machine learning (ML) into their software.
Data Poisoning
AI systems are, by nature, data-intensive. Additionally, data is now much more important than physical assets. Recent advances in AI have put the technology in the hands of millions of users. This massive influx has allowed users to casually use technologies like generative AI and large language models (LLM).
However, AI and ML models’ dependence on data also makes them vulnerable to adversarial attacks. Hackers are now more sophisticated than ever. They know how important data sets are to businesses and what damage corrupted data can cause.
Data Poisoning, in particular, poses a major challenge for AI models. In a data poisoning attack, the perpetrator manipulates or modifies an AI system’s training data, producing erroneous results. Deep learning is the process of learning by example. In this case, where the example itself is inaccurate, the model is bound to make errors unless the attack is identified and reversed.
Data poisoning in AI can be classified into three categories:
• Poisoning of data sets.
• Poisoning of algorithms.
• Poisoning model.
As the name suggests, these poisoning attacks range from turning small data sets into inaccurate data to replacing entire models with malicious ones.
Data poisoning has the same results as other anomalies in a model’s data sets, but they often stand out significantly and can be eliminated with strong security strategies.
Businesses need to integrate advanced verification and security systems into their processes to protect themselves from unethical hackers. An ideal security system to mitigate the new era of threats faced by AI must provide continuous monitoring and effectively analyze every entity that comes into contact with a business.
AI, being a relatively new technology, must be subjected to constant adversarial testing. Specific inputs should be prepared to help the AI model develop patterns against hacking attempts. Businesses also need to invest in security measures to detect and block malicious attacks. Finally, businesses should regularly monitor their data sets to ensure they are free of malicious activity.
Disinformation and manipulation
The generation and dissemination of incorrect information using AI is also a major concern. Thanks to AI, experts say it is possible to create and distribute a false story in a few seconds. Often, the sole purpose of data poisoning and adversarial attacks is to spread misinformation and manipulate the masses into believing false information.
Social networks is an important player here, as anyone can now generate and publish information. In contrast, trusted sources always verify the origin of a data set and guarantee its legitimacy. So people need to be careful about what they read. It is also up to tech giants, governments and AI developers to introduce best practices for standardized use of AI.
Cybercrime and deepfakes
As previously noted, AI models are a prime target for criminals and scammers, but these are entry-level penetrations. This rabbit hole goes even deeper as digital offenders now use AI technologies to commit more serious crimes.
Cybercriminals can use AI to create fake identities deceive people with elaborate scams. Additionally, thanks to AI, criminals can launch cyberattacks such as spear phishing attacks, denial of service attacks and swarm attacks. As AI models rely on continuous consumption of user data, hackers can create backdoors in the company’s data-centric processes and spy on users without their consent.
The challenge of backdoors and data breaches is not new; the only difference is that, thanks to AI, offenders can now commit these crimes on a larger scale.
Saving AI for the future
Securing AI models is an ongoing process that requires a proactive and scalable approach.
To meet these challenges, companies must integrate continuous monitoring into their processes. AI models should be tested daily, with experts analyzing their behavior. AI models also need to be updated based on emerging technologies.
Educating users and the general public about AI will also be an integral part of combating these challenges. The majority of the world’s population needs to better understand the purpose of AI. This gap can only be closed through the collective efforts of global technology leaders and governments.
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