AI technologies are changing industries fast and most companies are already using or will use AI in the next few years. While AI brings many benefits — increased efficiency, customer satisfaction and revenue growth — its also introduces unique risks that need to be addressed proactively.

From reputation damage to compliance violations and cyber attacks, the consequences of poorly implemented AI systems are severe. The rise of cyber-physical systems, like autonomous vehicles, highlights the need to integrate robust safety measures into AI development and deployment.

So experts have created practical recommendations to help organizations navigate these challenges. These guidelines are designed to make sure AI systems are secure, reliable and aligned with regulatory and ethical standards so businesses can use AI safely and responsibly.

Key risks to consider

With AI being applied in so many areas, businesses need to consider many risks:

Risk of not adopting AI
This may sound counterintuitive, but assessing the gains and losses of AI adoption is key to understanding and managing other risks.

Regulatory compliance risks
Rapidly evolving AI regulations make this a dynamic risk requiring frequent reassessment. Beyond AI-specific regulations, organizations must also consider associated risks, like violations of personal data processing laws.

ESG risks
These include social and ethical concerns surrounding AI and risks of exposing sensitive information.

Risk of AI misuse
From silly to malicious use cases, users will use AI in unintended ways.

AI models and training datasets threats
Attackers will target the data used to train AI systems and compromise their integrity.

Company services integrating AI threats
These will impact the broader IT ecosystem.

Data security risks
The data processed within AI-enabled services may be vulnerable to attacks.

The last three categories encapsulate the challenges of traditional cybersecurity in complex cloud infrastructures: access control, network segmentation, vulnerability management, monitoring, supply chain security and more.

Aspects of safe AI deployment

Safe AI deployment requires a balanced approach that combines both organizational and technical measures. These can be categorized into the following areas:

Organizational measures

Employee training and leadership education. Educate staff and leadership on AI risks and mitigation tools so you have an informed workforce to manage AI challenges.

Supply chain security. Scrutinize the source of AI models and tools. Make sure all resources come from verified, secure providers to reduce vulnerabilities.

Technical measures

Infrastructure security. A robust security infrastructure is necessary, incorporating identity management, event logging, network segmentation, and advanced detection tools like Extended Detection and Response (XDR).

Testing and validation. Thorough testing ensures AI models comply with industry standards, remain resilient to improper inputs, and meet specific business requirements.

Bias detection and correction. Detecting and addressing biases, especially when models are trained on non-representative datasets, is key to fairness and accuracy.

Transparency mechanisms. User-friendly systems for reporting vulnerabilities or biases helps organizations to build trust and improve AI systems over time.

Adaptation and compliance

Timely updates and compatibility management. Structured processes for updates and compatibility are needed to keep up with the fast pace of AI evolution.

Regulatory compliance. Staying aligned with emerging AI laws and regulations is an ongoing effort, requiring dedicated resources to ensure compliance with latest standards.

Practical implications

Deploying AI means focusing on risk management and security. Identifying vulnerabilities early through threat modeling allows businesses to address potential issues before they become problems. This proactive approach reduces the likelihood of costly mistakes and ensures smoother integration of AI systems.

A secure infrastructure is equally important. By implementing strict access controls and continuous monitoring, businesses can safeguard both their AI models and the IT environments they run in. Security measures must go beyond the models themselves, protecting the entire ecosystem that supports AI functionality.

Employee training plays a big role in responsible use of AI. Teams need to work effectively with these systems and leadership need to understand the risks and manage them. Proper preparation ensures a company-wide culture of accountability and awareness.

Thorough testing and validation of AI models are non-negotiable. These processes ensure that the systems perform reliably under different conditions and align with ethical standards. Testing uncovers weaknesses in data handling and decision-making, which can be addressed before the systems go live.

Supply chain security is another key element. Organizations must carefully vet their providers and ensure all AI models and tools come from trusted sources. This reduces the risk of vulnerabilities introduced through third-party dependencies. Addressing biases in AI models, such as those stemming from unrepresentative datasets, is equally crucial for maintaining fairness and reliability.

To maintain long-term system integrity, companies should have clear processes for reporting vulnerabilities. Allowing users and external experts to report issues improves system reliability over time. Regular updates and prompt fixes for compatibility issues are essential, as the fast pace of AI development means libraries and tools change quickly.

By integrating these practices, businesses can achieve a balance between leveraging AI’s potential and managing its inherent risks.

The road ahead

AI offers immense potential, but its successful and safe deployment requires foresight, planning, and continuous vigilance. By managing risks proactively, companies can harness AI’s benefits while minimizing its risks. Moreover, collaborative efforts between policymakers, industry leaders and researchers are essential to creating a safer and more innovative AI ecosystem.

Neural networks are creeping into every area of our lives: from big data analysis, speech synthesis, and image creation to controlling autonomous vehicles and aircraft. In 2024, Tesla added neural network support for autopilot, AI has long been used in drone shows to form various shapes and QR codes in the sky, marketers and designers use AI to generate illustrations and text.

After the release of ChatGPT at the end of 2022 and its popularity, many companies have been actively developing their services based on GPT models. With various services and AI-based bots, neural networks have become accessible to a wide range of users. But if you don't follow information security rules, using these services and neural networks involves certain risks. Let’s talk about those.

Risks of using neural networks

The euphoria caused by the discovery of GPT chat for many people has been replaced by caution. With so many services based on language models, free and paid, users have noticed that chatbots can provide unreliable or harmful information. Especially dangerous is incorrect information about health, nutrition and finances, weapon manufacturing, drug distribution and more. 

Moreover, neural networks are getting better and better and the latest versions can create incredibly realistic fakes, synthesizing voice or video. Scammers use these features to deceive their victims by forging messages and calls from acquaintances and videos with famous personalities.

The main threat is that many users trust neural networks and chatbots in general. Surrounded by an aura of accuracy and objectivity, people forget that neural networks can work with fictional facts, provide false info and generally make wrong conclusions. It has been proven many times that mistakes happen. If you ask silly questions, the damage will be minimal. But, if you use chatbots to solve finance or medicine issues, the consequences can be devastating. Plus, often to get an answer from a neural network, you need to provide some data.

A big question is what will happen to that data afterwards. No one guarantees that the information about you that you included in the queries will not subsequently appear somewhere on the darknet or become the basis for a sophisticated phishing attack.

In March 2024 bug hunters at Offensive AI Lab found a way to decrypt and read intercepted responses thanks to data encryption feature in ChatGPT and Microsoft Copilot. Regardless of how fast OpenAI patched this vulnerability, it’s a great example of how malicious actors can use API vulnerabilities to steal your data, including passwords or corporate info. And vulnerabilities can be used to DDoS the system and bypass protection.

There are several types of attacks on AI and it is important to know the difference. For example, evasion attacks (modifying of input data) are potentially the most common. If the model requires input data to work, it can be modified appropriately to disrupt the AI. On the other hand, data poisoning attacks are long-term. A trojan in the AI model will remain even after retraining. All this can be combined into adversarial attacks — a way to fool a neural network to produce an incorrect result.

Neural networks are not yet protected from attacks, data falsification and interference in their work for malicious purposes, so users should be aware and follow certain rules when working with chatbots.

Precautions and recommendations

The technology of large language models is rapidly developing, penetrating deeper into our lives, and gaining more users. To protect yourself and your data from potential threats, follow some rules when working with neural networks:

• Don’t share confidential info with chatbots;
• Download neural network apps and services from reliable sources;
• Verify the info provided by the chatbot.

Moreover, the main recommendation when working with public neural networks is not to assume that your dialogue with it is private. It's better to avoid a situation where the questions asked contain any private information about you or your company. The exception is if you are working with an isolated instance of a neural network, located in your environment and for which your company is responsible.

Also, check the services through which you interact with the neural network. An unknown channel im your messenger promising free work with all known LLM models definitely should't be trusted.

Companies, whose employees use neural networks at work, should be extra cautious. The interest of malicious actors in corporate data is higher, and they look for sensitive organizational information first and foremost.

The best way to protect against cyber threats is to have ongoing cybersecurity and AI training for employees. This is a must have in any workflow. Through training, it is possible to improve specialists' skills and, consequently, reduce the number of attacks by more than 70%.

Additional measures should also be taken to enhance the overall IT security of the company. First of all, you need to develop improved AI training algorithms considering its vulnerabilities, which will make the model more reliable by 87%. It is also necessary to "train" the neural network: to let it handle artificially created cyber attacks to improve the algorithm. This will help reduce the number of hacks by 84%. Moreover, it is necessary to constantly update software to reduce vulnerabilities by more than 90%.

Conclusion

Both companies and ordinary users already tasted the benefits of neural networks. In many areas, they help solve everyday tasks and save time and money. For example, generative neural networks affected the cost of making movies, TV series and other videos where graphics and processing are needed. At the same time, roughly the same neural networks have caused a wave of deep fakes, such as new variant of the Fake Boss attack. 

Every user must understand that the neural network is vulnerable. Just like a messenger, mailbox, or work task planner — it can be hacked or fail, so it is important to work with it consciously.

In the current digital landscape, where we frequently engage in conversations without visual context, our reliance on audio cues to verify the identity of our conversational partners has intensified. Our brains have developed an astonishing ability to discern and recognize the intricate details in someone’s voice, akin to an auditory signature that is unique to each individual. These vocal signatures, composed of elements such as pitch, pace, timbre, and tone, are so distinctive that we can often identify a familiar voice with just a few spoken words. This remarkable auditory acuity serves us well, but it is under threat by the advent of advanced technologies capable of simulating human voices with high accuracy—voice deep fakes.

What are deep fakes? 

The term 'deepfake' has quickly become synonymous with the darker potential of AI. It signifies a new era where artificial intelligence can manipulate reality with precision. Early deepfakes had their tells, but as the technology has progressed, the fakes have become almost indistinguishable from the real thing. 

The entertainment industry's experimentation with deep fakes, such as the lifelike replicas of celebrities in a TV show, serves as a double-edged sword. It showcases the potential for creative innovation but also hints at the perils of AI in the wrong hands, where the distinction between truth and fiction becomes perilously thin.

The creation of voice deep fakes is rooted in complex AI systems, particularly autoencoders, which can capture and replicate the subtleties of human speech. These systems don't just clone voices; they analyze and reproduce the emotional inflections and specific intonations that make each voice unique.

The implications are vast and varied, from actors giving performances in multiple languages without losing their signature vocal emotion, to hyper-personalized virtual assistants. Yet, the same technology also opens avenues for convincing frauds, making it harder to trust the unseen speaker.

The dangers of convincing voice deep fakes

Crafting a voice deepface is a sophisticated endeavor. It involves a series of complex steps, starting with the collection of voice data to feed into AI models. Open-source platforms have democratized access to this technology, but creating a voice deep fake that can pass for the real thing involves not just the right software but also an expert understanding of sound engineering, language nuances, and the intricate details that make each voice distinctive. This process is not for the faint-hearted; it is a meticulous blend of science and art.

The misuse of deepfake technology has already reared its head in various scams, evidencing its potential for harm. Fraudsters have leveraged these fake voices to imitate CEOs for corporate espionage, mimic government officials to spread disinformation, and even duplicate voices of family members in distress as part of elaborate phishing scams. These incidents are not simply one-off events but indicative of a troubling trend that capitalizes on the inherent trust we place in familiar voices, turning it against us.

The path that deepfake technology is on raises profound questions about the future of trust and authenticity. Currently, the most advanced tools for creating deep fakes are closely held by technology companies and are used under strict conditions. But as the technology becomes more accessible, the ability to create deep fakes could fall into the hands of the masses, leading to widespread implications. This potential democratization of deepfake tools could be a boon for creativity and individual expression but also poses a significant threat in terms of misinformation, privacy, and security.

The defense against deep fakes: a multifaceted approach

To tackle the challenge of deep fakes, a robust and varied approach is essential. Researchers are developing sophisticated detection algorithms that can spot signs of audio manipulation that are imperceptible to the human ear. Legal experts are exploring regulatory measures to prevent misuse. And educational initiatives are aiming to make the general public more aware of deep fakes, teaching them to critically evaluate the media they consume. The effectiveness of these measures will depend on their adaptability and continued evolution alongside deepfake technology.

Awareness is a powerful tool against deception. By educating the public on the existence and methods behind deep fakes, individuals can be more vigilant and less susceptible to manipulation. Understanding how deep fakes are made, recognizing their potential use in media, and knowing the signs to look out for can all contribute to a society that is better equipped to challenge the authenticity of suspicious content. This education is vital in an era where audio and visual content can no longer be taken at face value.

Navigating the ethical landscape of deepfake technology is critical. The potential benefits for creative industries, accessibility, and personalized media are immense. Yet, without a strong ethical framework, the negative implications could be far-reaching. Establishing guidelines and best practices for the responsible use of deepfakes is imperative to prevent harm and to ensure that innovation does not come at the cost of truth and trust.

Conclusion

As voice deep fakes become more advanced, they pose a significant challenge to the trust we place in our auditory perceptions. Ensuring the integrity of our digital communications requires not just caution but a comprehensive strategy to navigate this new terrain. We must foster a society that is equipped to recognize and combat these audio illusions—a society that is as critical and discerning of what it hears as it is of what it sees. It is a complex task, but one that is essential to preserving the fabric of trust that binds our digital and real-world interactions together.

The marvels of modern computing are, in part, thanks to advances in artificial intelligence. Specific breakthroughs in large language models, such as OpenAI's GPT-4 and Google's BERT, have transformed our understanding of data processing and manipulation. These sophisticated models masterfully convert input data—whether it be text, numbers, or more—into a form that machines can understand. This intricate process, known as data encoding, serves as the foundation for these models to comprehend and generate human-like text. Let's delve deeper into the intricacies of data encoding and how it powers the magic of AI language models.

The secret code of machines

The beginning of the journey involves comprehending how GPT-4 or BERT processes sentences typed into them. Contrary to human processing capabilities, these models can't directly interpret words. Instead, they employ something known as word embeddings. This complex yet efficient technique transforms each word into a unique mathematical form—akin to a secret code decipherable only by machines. Each encoding is meticulously performed to ensure that semantically similar words receive comparable codes. The aim is to create a rich, multidimensional landscape where each word's meaning is determined by its location relative to other words.

The role of positional encoding in context understanding

While individual words carry their importance, the structure of language extends beyond isolated entities. The sequence of words, the context, can drastically alter the meaning of a sentence. To illustrate, consider the phrases "Dog bites man" and "Man bites dog." The same words are used, but their arrangement creates entirely different narratives. That's where positional encoding enters the picture. By assigning each word an additional code indicating its position in the sentence, positional encoding provides models with a vital understanding of language structure and syntax.

The attention process: making words context-aware

After word and positional encoding, these mathematical representations, or word embeddings, undergo an 'attention' mechanism. Here, each word embarks on a figurative group discussion with all the other words in the sentence. During this interaction, each word decides the importance it should attribute to the others. For instance, in the sentence "Jane, who just moved here, loves the city," the word "Jane" would assign significant attention to "loves."

These 'attention' weights are then used to compute a new representation for each word that is acutely aware of its context within the sentence. This batch of context-aware embeddings journeys through multiple layers within the model, each designed to refine the model's understanding of the sentence. This systematic processing prepares the model to generate responses or predictions that accurately reflect the intended meaning of the sentence.

GPT-4: writing text one word at a time

GPT-4 has adopted a unique approach when it comes to generating text. It operates on a "one word at a time" principle. Beginning with an input, it predicts the next word based on the preceding context. This predicted word is then included in the context for predicting the following word, and the process repeats. This strategy allows GPT-4 to produce text that is not just grammatically coherent, but also semantically relevant, mirroring the way humans write one sentence after another.

BERT: a 360-degree view of sentence context

BERT, on the other hand, possesses a distinct capability that sets it apart from other models. It can process and understand text in both directions simultaneously. BERT does not limit itself to considering words before or after a given word. Instead, it absorbs the entire context at once, effectively offering a 360-degree view of the sentence. This bidirectional understanding enables BERT to comprehend the meaning of words based on their complete context, significantly enhancing the model's ability to interpret and generate nuanced responses.

The versatility of data encoding

While language forms a significant chunk of these models' use cases, they aren't confined to it. An exciting feature of models like GPT-4 and BERT is their ability to work with any kind of sequential data. This characteristic opens up a universe of possibilities for diverse fields, from composing harmonic music to decoding complex genetic sequences, predicting stock market trends, or even simulating game strategies. By analyzing patterns in the sequential data, these models can unearth hidden insights and produce creative outcomes, making them an invaluable asset in numerous areas beyond language processing.

Expanding horizons: applications and future prospects

The wonders of data encoding do not stop with text generation. In fact, the potential applications of these AI models are continually expanding. They can be used to aid human decision-making in complex scenarios, such as medical diagnosis or legal analysis, by digesting massive amounts of textual data and making informed suggestions. In the field of research, they can help summarize lengthy academic papers or generate new hypotheses based on existing literature. The entertainment industry isn't left out either, as these models can create engaging content, ranging from writing captivating stories to generating dialogues for video games.

Moreover, GPT-4 and BERT's remarkable abilities to understand and manipulate language are catalyzing research into other AI models. Researchers are exploring ways to combine the strengths of various models and reduce their limitations, which promises an even more exciting future for AI.

Conclusion

In conclusion, data encoding in AI models like GPT-4 and BERT can be likened to watching a symphony of processes working in perfect harmony. From word embeddings and positional encoding to attention mechanisms, these models leverage a series of intricate techniques to decode the hidden patterns in data, transforming it into meaningful information. The incredible capability of these models to understand context, generate human-like text, and adapt to diverse data types is revolutionizing the field of artificial intelligence, paving the way for a future brimming with AI innovations.