How to boost digital defense and security for chatbots

Feb. 23, 2024
The speed of chatbot adoption has outpaced the expansion of cybersecurity programs to protect against threats introduced by these technologies.

The interface between humans and machines has significantly improved over the past few years. With advancements in chatbot technology, these interactions have taken on a more natural flow. Systems can now discern intentionality and extract nuances that can be more readily turned into actions to perform or the selection of targeted information to return to the user.

This evolution holds substantial benefits for businesses, leading to enhanced customer service and more efficient utilization of human resources. In turn, the time saved and revenue dollars gained as a result of chatbots have driven mass adoption of the technology across a wide array of businesses and industries.

However, the speed of chatbot adoption has outpaced the expansion of cybersecurity programs to protect against threats introduced by these technologies. Traditional cybersecurity attacks, which seek to pilfer sensitive data and establish persistent access to corporate networks, have found new avenues for exploitation within the intricate layers introduced by chatbot systems. Not only does this set the stage for chatbots to be on the fringe of many security programs, it risks a business’s brand loyalty with millions of customer data points at stake.

So how can businesses rest assured they can incorporate chatbots into their systems without risking valuable customer and financial data? The answer is simple: AI technology developers must build security measures into the chatbots themselves. With safeguards rooted in the very foundation of the algorithm, sensitive data is much more secure.

Let’s dive into the best practices and tactics that every AI developer should bear in mind while creating an AI algorithm.

Protect your AI data like gold

Securing AI data is of paramount importance in today's digital landscape given the growing reliance on AI and machine learning in various applications. This can be ensured first and foremost with encryption.  Sensitive data used to train AI models, such as customer information, financial records, or proprietary business data, should be encrypted both in transit and at rest. It ensures that even if an unauthorized party gains access to the data, they cannot read or use it without the encryption keys.

Equally important is implementing strict access controls to limit who can access and manipulate the data. This includes role-based access control (RBAC) and the principle of least privilege (PoLP), where individuals only have access to the data necessary for their roles. Data loss prevention (DLP) measures also aim to detect and prevent data breaches by monitoring data flows and implementing mechanisms to block or alert administrators when sensitive data is at risk of being leaked or accessed by unauthorized parties.

Beyond these preventive measures, organizations should also have a well-defined incident response plan in place. This plan outlines how to react in the event of a security breach, including steps to contain the breach, notify affected parties, and recover from the incident. Key to any such plan should also be regular employee training, covering the importance of AI data security and each employee’s role in maintaining it. Human error is often a significant factor in data breaches, so training and awareness programs can mitigate this risk.

Implement robust authentication

While it may seem like an obvious answer, multi-factor authentication (MFA) is a crucial security measure for AI systems and, frankly, any systems with sensitive data. It requires users to provide two or more forms of authentication before they can access the system. This typically includes something the user knows (like a password), something the user has (like a mobile device or security token), and something the user is (biometric data like fingerprints or facial recognition).

MFA significantly enhances security by adding an extra layer of protection. Even if an attacker manages to steal a password, they would still need the second factor to gain access. That said, users should be discouraged from using SMS as multi-factor authentication, as it's not as safe as compared to authenticator apps.

Beyond just requiring strong passwords and MFA, it's essential to have robust user authentication and authorization mechanisms in place. This ensures that users are who they claim to be and that they have permission to access specific AI algorithms, datasets, or functionality. User credentials should be stored securely, and user sessions should be managed to prevent unauthorized access. And, as a failsafe, account lockout policies that temporarily lock out users after a certain number of failed login attempts should also be implemented.

Teach your AI to be vigilant

Training AI for threat detection in cybersecurity is an essential component of modern defense strategies. AI can be a powerful ally in identifying and responding to various cyber threats, including malware and phishing attempts. To effectively do this, it's important to understand the evolving cybersecurity threat landscape.

Threat actors continually develop new tactics and techniques, so staying informed about the latest threats is crucial. Start by collecting large datasets of cybersecurity-related data, such as network traffic logs, email content, and historical records of attacks. This data should be labeled to indicate whether an event or item is benign or malicious. What’s more: it must be done constantly. The threat landscape evolves, so AI models must be continuously trained and updated. Ongoing monitoring and training are necessary to ensure that the system remains effective at detecting new threats and adapting to changes in attack techniques.

Anomaly detection and ensemble learning are other key features that AI can be trained for. Anomaly detection involves identifying deviations from established baselines and is valuable for identifying previously unknown threats or zero-day attacks. Ensemble learning involves combining multiple AI models to enhance the overall detection accuracy. This approach can improve the resilience of the system by reducing the risk of false positives and false negatives.

Like all technologies, chatbots will continue to evolve and revolutionize the way humans and machines interact. On a similar trajectory, the cyber risks and targeting of these technologies will also continue to rise. A proactive approach that instills core cybersecurity practices can help ensure a smooth adoption that avoids significant, costly cybersecurity incidents.

By putting in the legwork up front and following a few simple steps, technology developers can create chatbots that have the potential to drive incredible productivity, saving countless hours and dollars to boost the bottom line.


Michal Oglodek is the CTO and co-founder of, where he is responsible for overseeing the development of the company's AI platform. He leads a team of talented developers and data scientists, designing and developing the algorithms and models that power's chatbots. With over 15 years of technology experience under his belt, Oglodek has made a name for himself in the artificial intelligence industry and has played a pivotal role in the development of's platform.

Under Oglodek's leadership, has made significant advancements in the field of AI and SaaS solutions. He is committed to using the latest technology to create solutions that improve the customer experience and democratize access to information. He believes that chatbots have the potential to transform the way organizations communicate with their customers, providing them with a more personalized experience.