Is Conversational AI a new threat vector for Cyber Security attacks?

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Conversational AI in simple terms is the ‘brains’ of a system that can converse like a human to a human. These AI systems are made up of sophisticated technologies that enable the computer system to simulate real conversations that engage human beings. Both Chatbots and Conversational AI are used interchangeably. However, Conversational AI falls under the broader category of AI that uses ‘voice bots’ as well as ‘textbots’, and Chatbots are limited to ‘text-only’ conversations. Conversational AI systems provide a straightforward channel for users to identify problems and resolve their concerns. Conversational AI has various business benefits ranging from service resolution with reduced costs and better operational efficiencies to capturing conversational data to gain insights into consumer demands. Other advantages include, increase in revenue by providing personalized and proactive experiences, enhanced functions, and automation of processes in addition to instant and accurate support to solve complex problems thus improving the overall service quality.

Conversational Intelligence
Language is a method of human communication that involves many complex nuances such as word meanings, slangs, clarifications, and interruptions. These intricate nuances of language influence the meaning of the conversation and predict the importance of the expression. Conversational AI uses various language attributes to simulate real-life conversations that enable better user engagements.

  • Management of Context
    Conversations among individuals often differ based on their relationship, comfort level, and knowledge of the topic of conversation. Conversational AI is built with systems that store key information from past interactions, basic user information, and user preferences. This attribute of the chatbot enables personalized conversations and superior user interaction.
  • Dialog Management
    Most of the humans use complex connotations while expressing themselves. Machine learning engineers have developed Conversational AI and Chatbots with abilities to interpret the twists and turns in the human dialogs. Conversational AI systems are equipped to process multiple intents in single utterances. These can analyze the time required for pausing and resuming conversations and also processing dynamic dialogs.
  • Multilingual conversations
    As modern business functions on a global level, conversations and interactions also occur for the global market. Conversational AI is modeled used trained data that can interpret and process every major language in the world, helping businesses to expand globally. The use of global language by the conversational AI is fruitful for small and large business as it serves a large number of customers. Conversational AI comes with features that provide businesses to personalize their systems by choosing the language of interest, defining intents, and many more.
  • Sentiment analysis
    Conversations are incomplete without the appropriate use of emotions and tone. Conversational AI has in-built systems that allow the Chatbots to recognize triggers such as connotation and word usage to identify the type and intensity of emotion in a conversation. This feature helps assess correct inputs and steer conversational that are user friendly, thus enabling better solutions to the users or customers.

How does Conversational AI work?
Conversational AI is built using complex Machine learning and Deep learning algorithms that continually learn and adapt to understand every expression by the user. Conversational AI is engineered by employing simple exchanges through pre-built conversational pathways. Techniques such as Natural language processing is used to interpret user messages and intents. Neural conversational models are utilized to create chatbots that can recognize user queries by training the model. Conversational AI systems ask a variety of queries to the users if some part of the request is unclear. A good conversational AI is equipped to handle open-ended queries and can easily adapt to the context and word usage by the consumers. Chatbots can easily understand the direction of the conversation and generate appropriate responses to the user queries and resolve problems.

Conversational AI as a threat vector:
Interactions with Chatbots and Conversational AI is a much-appreciated use of  technology. However, conversations with AI-powered systems permit the transfer of a large amount of data, which is likely to be used for malicious attacks. Attackers can program or train Chatbots and Conversational AI to maintain engaging conversations with users while slyly influencing them to disclose financial and personal information. Malware installations in conversational AI can also deceive consumers and
appear as friendly and secure systems.
AI-powered Chatbots can cripple human interactions by creating a detour in the phone and online gateway support systems, thus easily obtaining secure consumer information. If conversational AI is built using poor secure systems, it can effectively be hacked by cyber-security attackers. By using histograms, Natural language processing systems, and manipulating publicly accessible data, attackers can direct the cyber-security attacks and burden both the companies and the users. Cyber-security threats can easily escalate as AI-powered systems educate themselves and evolve indirectly providing attackers with a great advantage.

Cyber-security attacks on Conversational AI can occur in the following ways:

  1. Threats incorporated in the background: Malicious adversarial threats can often maintain a subtle and friendly presence in the background for a long time. These are blended so well that they go unnoticed and are very difficult to detect. The attackers are extremely cautious while evading security controls of the AI-powered systems. Such conversational AI can easily be manipulated and can easily
    disguise itself in the digital environment. Cyber-security attackers install AI-powered malware that transfers large amounts of data, manipulate conversations and deceive users, while being digitally undetected.
  2. Quick attacks: This type of cyber-security attack requires skill and presents with more effective consequences on the conversational AI systems. Machine learning powered systems can be manipulated in a tailored pattern to convert once-friendly AI into offensive AI. Conversational AI systems are constantly learning and adapting to user requirements, which makes them vulnerable to cyber-security attacks. The attackers can manipulate the learning AI systems and change the security controls which can pose a threat to the business as well as users. Conversational AI system attacks can be personalized and made target specific due to their ability to understand and interpret the context used in conversations.
  3. Impersonating users: Conversational AI is susceptible to cyber-security attacks because of the very features that make them convenient for the users. Cyber-security attacks utilizing AI-malware can learn the nuances, word usage, language, and behavior and easily misguide the users. Such malware can analyze email and social media communications of the target audience and replicate the user’s writing style to craft messages or conversations that can breach the security of the user or company data. Attackers can also manipulate sophisticated friendly AI to become ‘Evil Bots’ that hurt human sentiments and provide wrongful information or solutions to the problems faced by the users. Maliciously crafted conversations by the AI-malware are almost impossible to distinguish from genuine conversations thus making conversational AI and Chatbots vulnerable to malevolent cyber-security attacks.
  4. Impersonating Chatbots: Conversational AI can relate to the variations in the language nuances and accordingly respond to the users. These Chatbots are always in the learning phase as human conversations are only predictable to an extent. Lack of appropriate security or admin’s credential being comprised is considered as a great opportunity for attackers as such security negligence renders the conversational AI extremely vulnerable to malicious attacks. Hackers can high-jack or deliberately impersonate chatbots.
  5. Man-in-the-middle attacks: In this type of cyber-security attack, attackers can simply put themselves in the middle of a conversation between the conversational AI and the user. This enables the attacker to eavesdrop and gather information or manipulate the conversation itself. MITM cyber-security attacks are typically used to steal financial credentials, credit card details, and passwords from the users. This is either directly carried out by the attacker or by using AI-powered malware. Man-in-the-middle attacks are preferred by attackers to steal secure information by deceiving users to perform fraud and malicious cyber-security attacks.
  6. Open Computer Attacks: When users lose their phones or computers or just leave the chatbot window unlocked, they provide an opportunity for the attackers to scrap in sensitive information and personal or client data by simply deceiving the conversational AI. This may be perceived as human error, but it is a chatbot lagging issue.

Conclusion
The cyber-security of Chatbots and conversational AI systems is not all grim, as one can undertake appropriate safety measures that restrict vulnerabilities to cyber-security attacks. It is ideal for both human and artificial intelligence to work together to restrict and eliminate malicious cyber-security attacks and potential cybercrimes. A better understanding of the deep learning technology can also enable us to analyze the vulnerabilities within AI and ML-powered systems. Furthermore, investing in good and secure systems to protect and safeguard the conversational AI chatbots can help bring down potential dangers and enable augmented cyber-security.