Improving Security & Data Redaction for Support Chatbots

Updated on May 15, 2025

In 2024, McKinsey estimated that generative AI will become a part of 50% of the existing customer operations infrastructure, resulting in over $400 billion in increased revenue.

As an executive in an enterprise, you’d have seen this play out in real time. According to a global survey, over 78% of enterprises use generative AI in at least one business function. 

Most of these business use-cases evolve around automating customer service operations with chatbots. Generative AI chatbots can help customers faster and with more accuracy.

This has led to efficiency gains across different verticals and introduced new security vulnerabilities to conquer. International organizations like OWASP (Open Web Application Security Project) have identified threats and vulnerabilities with generative AI applications. Some of these risks include sensitive data exposure, prompt injections, model poisoning, etc. These risks make the CISO job harder than ever.

When balancing efficiency and safety, it’s tough to push board decisions towards safety. But, there’s a strategy that changes this challenge into an opportunity:

“Since 76% of US consumers are not ready to share their data with AI providers, chatbot security can be a potential USP and differentiator.”

This article will take you through this hypothesis and also provide you with actionable insights that you can use to make your chatbot infrastructure safer. We’ll cover:

1. What are the Risks of AI Chatbots?

2. How to Ensure that the AI Chatbot Doesn’t Store Sensitive Data?

3. How to Turn Chatbot Security into Brand Trust?

4. How to Connect Chatbot Security to Operational Efficiency?

5. Implementing Chatbot Security: What are the Best Practices for CIOs and CISOs?

6. What are Some Future Trends in Chatbot Security?

What are the Risks of AI Chatbots?

AI chatbots are being adopted en masse and are primarily powered by Large Language Models (LLMs). While this significantly improves customer support efficiency, it also has some distinct security risks that your board and C-suite must be aware of. 

In 2024, OWASP compiled a list of ten vulnerabilities that can affect your chatbot-based operations, these include:

1. Prompt Injections

Most LLMs are trained not to practice malicious behavior; for example, if you ask ChatGPT for instructions on how to build a bomb, it will directly refuse. 

However, this can be manipulated. Malicious actors often use “prompt injection” methods to make the AI model overlook security guidelines. This could lead to:

• Unauthorized Data Access: Tricking a support bot into querying private data stores it shouldn’t access.
• Sensitive Information Disclosure: Coaxing the bot to reveal system prompts, infrastructure details, or confidential data.
• Unauthorized Actions: Manipulating the bot to execute commands in connected systems or perform functions beyond its remit (e.g., sending emails, modifying data).
• Content Manipulation: Forcing the bot to generate incorrect, biased, or harmful outputs.

2. Sensitive Information Disclosure

Sometimes chatbots can reveal sensitive data about your customers. This includes:

• Customer Data Leaks: Revealing Personally Identifiable Information (PII), financial details, or health data.
• Proprietary Information Loss: Exposing confidential business data, algorithms, or details about the training data itself.
• Accidental User Disclosure: Users themselves might unintentionally provide sensitive data that the chatbot later discloses.

3. Compromised Supply Chains & Data Integrity

Most businesses rely on third-party providers for AI infrastructure, which has some potential risks. These include:

• Supply Chain Risks: Infected datasets, outdated software, and cloud providers in core LLM infrastructure can affect your chatbot’s performance. 
• Data and Model Poisoning: Attackers can deliberately manipulate training or fine-tuning data to introduce biases, or vulnerabilities, degrading performance, or leading to harmful outputs.

4. Improper Output Handling

AI agents are often granted access to sensitive information (including transactional and PII data). If there are not enough guardrails around how these systems use this data, it can lead to problems:

• Downstream System Compromise: If the chatbot’s output isn’t validated before being passed to other systems, it can enable attacks.
• Excessive Permissions Risk: Granting the chatbot unnecessary capabilities (e.g., write access when only read is needed) creates opportunities for exploitation, especially via indirect prompt injections, allowing it to perform damaging actions beyond its intended scope.

5. System Prompt Leakage and Vector Database Vulnerability

Your chatbots get their instructions from a system prompt, and most likely use Retrieval-Augmented Generation (RAG) for their functions. While these methods are usually secure, there can be some risks:

• System Prompt Exposure: Leaked instructions used to guide the bot can reveal sensitive API keys, database details, or internal rules, aiding attackers in planning further exploits.
• RAG Vulnerabilities: Systems using Retrieval Augmented Generation (RAG) face risks related to how data vectors and embeddings are handled, potentially leading to unauthorized access, data leakage, or embedding manipulation.

6. Misinformation and Hallucinations

Large Language Models can hallucinate and generate incorrect information. This can lead to:

• Reputational Damage: Providing incorrect information erodes customer trust.
• Legal Liability: Factual inaccuracies (e.g., incorrect policy information) can have legal consequences, as seen in real-world cases.
• Unsafe Recommendations: Generating insecure code suggestions or downplaying serious issues (like health concerns).

7. Excessive Resource Consumption

The computational costs around AI deployments can be significant, and not having proper guardrails around usage can cost your business a lot. This makes AI systems vulnerable to attacks like:

• Denial of Service (DoS): Flooding the chatbot with requests or resource-intensive queries can render it unavailable.
• Economic Drain (“Denial of Wallet”): Uncontrolled usage can lead to unexpectedly high operational costs.
• Model Theft: Excessive querying can sometimes be used to extract or replicate the underlying model.

These threats underline the number of security measures we need to take with new chatbot implementations. The following section will explore how to mitigate these risks at scale. 

How to Ensure that the AI Chatbot Doesn’t Store Sensitive Data?

The core security risk around the threats we outlined above is the exposure of sensitive data. This can happen through various routes, and we will discuss the mitigation strategies for each one.

1. Direct User Inputs and Unredacted Logging

If a user explicitly shares sensitive data (passwords, OTPs, etc.) during their chatbot interaction, the data can be exposed in two ways. Your chatbot might remember the data if you use these interactions (for fine-tuning, training, etc.) without redaction. Secondly, malicious actors will be privy to sensitive information if they breach your chat logs. 

Mitigation Strategies 

•  Real-Time Data Redaction – Use a redaction technology that masks PII data before it goes through to the LLM or your logs. This will limit access and improve your security process overall. 
• Data Minimization – Explicitly train customers and agents not to reveal sensitive data during interactions. 
• Log Management – Log databases should have strict access controls, encryption processes, and retention policies.
• General Data Governance – If your business will functionally use customer conversations to train chatbots, ensure that the unfiltered data is never used directly for training. 

2. Sensitive Data in Chatbot Responses

Sometimes, the chatbot might include sensitive information in its replies, perhaps after retrieving it from a backend system. Similar to user inputs, if these responses are logged without redaction, they can contaminate future training data or expose sensitive details if logs are breached. Furthermore, a flaw could cause the chatbot to reveal sensitive data directly during the live interaction.

Mitigation Strategies

• Real-Time Output Redaction: Apply redaction technology to the chatbot’s outgoing messages before they are displayed or logged, masking sensitive patterns.
• Backend Access Control: Limit the chatbot’s access permissions within your databases and APIs to the absolute minimum data required for its function (Principle of Least Privilege).
• Model Safety Guardrails: Fine-tune the chatbot model with instructions not to request or repeat sensitive information, and add application-level filters to check responses.
• Secure Log Management & Governance: Apply the same strict access, encryption, and retention policies to logs containing bot responses, ensuring anonymization before any training use.

3. Model Training on Corrupted Data

The large models (like GPT-4-o, Claude) on which your chatbot might be built are trained on vast internet datasets. Occasionally, these datasets might contain sensitive information scraped from public sources (like old data breaches). While rare for specific user data, the model might have “memorized” some of this data and could potentially reveal it if prompted correctly.

Mitigation Strategies

• Vendor Due Diligence: Carefully vet your LLM provider’s data sourcing, cleaning, and training practices. Prioritize vendors with firm privacy commitments.
• Output Filtering: Implement checks in your application layer to scan the chatbot’s final responses for patterns resembling sensitive data, catching potential memorized information.
• Safety-Focused Fine-tuning: Use your curated, safe datasets to fine-tune the model, guiding it towards safer response patterns and potentially suppressing undesirable learned behaviors.

4. Compromised Supply Chains or Data Poisoning

Malicious actors could intentionally insert insufficient data or vulnerabilities into the training datasets (either the foundational model’s or your fine-tuning data) or third-party software components used by the chatbot. This “poisoning” can introduce biases, create backdoors, or cause the model to leak specific information when triggered.

Mitigation Strategies

• Data Integrity Checks: Use training data from trusted sources and implement checks (like anomaly detection) to spot irregularities before training.
• Secure Supply Chain Practices: Scrutinize all third-party models, libraries, and data sources. Monitor for known vulnerabilities in your chatbot’s software components.
• Continuous Model Monitoring: Keep an eye on the chatbot’s behavior in production for sudden changes, biases, or unexpected outputs that might signal a compromise.

5. Prompt Injections

Attackers can use clever prompts to trick the chatbot into ignoring its safety rules and revealing sensitive information it has access to (from the conversation, connected systems via RAG, or potentially insecure logs/prompts). The data is exposed directly in the chatbot’s output.

Mitigation Strategies

• Input Sanitization: Analyze user inputs for patterns that look like attempts to inject commands or override instructions.
• Defensive Prompt Engineering: Structure your system prompts carefully to separate instructions from user input, making it harder for the model to get confused.
• Output Filtering: Validate the chatbot’s response before showing it to the user, checking if it contains unexpected sensitive data or seems to be executing unintended commands.
• Limit LLM Capabilities: Restrict the tools and permissions the chatbot can use contextually, reducing the potential damage if an injection attack is successful.

6. System Prompt Leakage

System prompts are crucial and well-researched and might include sensitive business information, API keys, and account passwords. If an attacker manages to leak these prompts (often via prompt injection), they gain valuable information to launch further attacks aimed at accessing sensitive data stores.

Mitigation Strategies

• Avoid Hardcoding Secrets: Never embed passwords, API keys, or specific database connection strings directly within the system prompt text.
• Use Secure Secrets Management: Store necessary credentials in a dedicated secrets vault (like Azure Key Vault, AWS Secrets Manager) and have the application retrieve them securely only when needed.
• Defend Against Prompt Injection: Since prompt injection is the primary way system prompts get leaked, it is essential to implement strong defenses against it (see previous point).

7. Inadequate Access Control in RAG

If your chatbot uses Retrieval Augmented Generation (RAG) to pull information from knowledge bases or databases, weak access controls on those backend sources are a significant risk. The chatbot might retrieve and expose sensitive data it shouldn’t see, or potentially leak data between users in a shared environment.

Mitigation Strategies

• Granular Data Store Access Controls: Enforce strict, user-specific permissions on the underlying data sources accessed by the RAG system.
• Contextual Filtering & Tenant Isolation: Ensure the RAG process only searches and retrieves data relevant to the current user and their permissions, preventing cross-user data leakage.
• Audit RAG Activity: Monitor and log which data the RAG system is accessing and retrieving to spot suspicious activity or policy violations.

Now that we understand the security measures you can take, let’s talk about the strategy that will guide these efforts. The following section focuses on how security can help you build brand trust.

How to Turn Chatbot Security into Brand Trust?

According to IAPP, around 57% of US consumers agree that AI poses a privacy risk to their workflows. This means that while AI delivers operational efficiency to your organization, it can alienate your users. 

CISOs and marketing teams can work together to mitigate this. The second push should be transparency and clarity after implementing the security measures described earlier. Having a two-way conversation with customers will help you build a better relationship with them while keeping your efficiency gains intact.

Most of our enterprise customers use the following tactics for this:

1. Be Transparent about Cybersecurity – Security measures are often opaque to customers. Most of this is by design, and we’re not asking CIOs and CISOs to detail their security measures on paper. However, clear communication about cybersecurity measures in the business helps your customers feel protected.
   You should explicitly discuss the broad measures in your privacy policy, add UI nudges that tell your customers about data protections, and maintain an accessible set of technical documentation on security measures. 
2. Focus on the Benefits – Instead of discussing algorithms and actual technical processes, focus on how your technology makes customers safer. For example, you can talk about how real-time redaction helps mask their credit card numbers, addresses, and other sensitive information from AI exposure. 
3. Security as Brand Trust – Use your customer communications to reiterate that your first concern will always be your customers’ comfort and privacy. Customers will trust your operations more when you enforce a data protection-first policy.

While these are simple measures, they are essential. Customers don’t trust AI with information security; your commitment to that will drive adoption and brand trust. 

We can also see this increase in brand trust and operational efficiency, and we’ll talk about that in the next section. 

How to Connect Chatbot Security to Operational Efficiency?

Brand trust from chatbot security is not a “soft” benefit. It helps retain more customers and drive adoption while increasing operational and cost efficiencies. This happens in the following ways –

1. When customers trust that the AI is safe (due to your security messaging) and effective (general training efficiency), they are more likely to use the chatbot for their questions, translating to more effective deployments and faster adoption. 
2. Effective data processes help your agents perform their work better. If technology already protects sensitive data, human agents can focus on faster resolutions without spending too much time mitigating possible data leakage risks. 
3. Strong data security policies also lead to strong compliance adherence. This makes you more acceptable to any enterprise or privacy-aware customer. You automatically pass through compliance checks around HIPAA, PCI-DSS, and GDPR, and you can avoid potential fallout from security breaches. 
4. Automatically redacted and anonymized data from customer conversations gives you a great database that you can use to train your chatbots. These processes (fine-tuning, better knowledge bases) improve your AI efficiency with time. 

It’s hard to quantify security as a revenue center. Still, it works as one in the context of customer-facing AI. Trust helps faster adoption and iterations while preventing costly security breaches and helping agents work better. 

While we’ve outlined the techniques to improve chatbot and AI security, we’ve yet to outline the best practices that CIOs and CISOs can use as they build this infrastructure. In the next section, we’ll return to the article’s focus and showcase the best tried-and-tested practices we recommend to the CIOs and CISOs that buy from us. 

Implementing Chatbot Security: What are the Best Practices for CIOs and CISOs?

We’ve discussed the risks, the importance of redaction, and how security builds trust and efficiency. Now, let’s focus on the practical implementation. Building and maintaining a secure chatbot infrastructure requires deliberate best practices beyond selecting a vendor. Based on established cybersecurity principles adapted for AI, here are the key practices we recommend to the CIOs and CISOs we work with:

1. Enforce Strict Access Controls (Least Privilege): You should treat AI like any other critical system and limit access. Define clear roles (e.g., developers, admins, security analysts) and grant permissions based strictly on job requirements. This applies to:
   • Human Access: Who can configure the chatbot, access sensitive logs, manage system prompts, or review training data?
   • System Access: Which backend systems, databases, or APIs can the chatbot access? Grant only the minimum necessary permissions for its function.
2. Implement Continuous Auditing & Monitoring: Security isn’t a one-time setup. You need ongoing visibility:
   • Regular Reviews: Periodically audit chatbot configurations, system prompts (checking for embedded secrets), redaction rule effectiveness, and overall security posture.
   • Real-Time Monitoring: Monitor chatbot outputs for anomalies, potential data leakage, signs of prompt injection attacks, or unexpected behavior. Log analysis is crucial here.
   • Automated Tools: Leverage LLM security scanners or monitoring tools where appropriate to automate parts of this process, helping to detect emerging threats or configuration drift quickly.
3. Establish Clear AI Governance & Ethical Use Policies:
   Define the rules of engagement for AI within your organization:
   • Data Handling Protocols: Create robust processes detailing how conversational data is collected, redacted, stored, used (especially for training), and deleted.
   • Ethical AI Guidelines: Promote ethical AI usage, outlining acceptable and unacceptable uses of the chatbot technology, focusing on data privacy and compliance. Ensure business units align on usage and address potential pain points within this framework.
   • Internal Transparency: Document known risks associated with your chatbot implementation and the mitigation steps. Make this information available to relevant internal stakeholders (security, legal, compliance, development teams) to foster proactive security awareness.
4. Invest in Workforce Training & Skills:
   Your team is a critical part of your security posture:
   • Technical Staff: Ensure developers, security analysts, and IT operations teams receive specific training on LLM/chatbot security risks, secure coding practices for AI applications, and the tools you implement.
   • End-Users/Agents: Provide training or clear guidelines to customer service agents and potentially end-users on interacting safely with the chatbot, especially regarding sensitive data sharing.
   • Address Skills Gaps: If internal expertise is lacking, consider hiring external specialists for training or consultation on specific chatbot security challenges.
5. Prioritize Robust Technology Choices (Implicitly Critical):
   While policies and processes are vital, they must be supported by capable technology. As emphasized earlier:
   • Effective Redaction: Ensure your chosen solution provides accurate, real-time redaction for inputs and outputs, covering relevant PII/PHI/PCI and custom data types.
   • Secure Integration: Verify that the chatbot platform integrates securely with your existing systems (CRM, databases, etc.).

By embedding these best practices – governing access, continuously monitoring, setting clear policies, enabling your workforce, and using the right tools – CIOs and CISOs can build an efficient, engaging, fundamentally secure, and trustworthy chatbot ecosystem.

In the next section, we will push the envelope further and discuss emerging security technologies around AI that you should be aware of. 

What are Some Future Trends in Chatbot Security?

Every security professional knows that the scope of cybersecurity is constantly increasing. And for fast-moving technologies like AI, being ahead of the trends is your business’s best competitive advantage. 

Here are some key trends in AI security that you should monitor:

1. Focus on the Application Ecosystem

Most AI research has been understandably focused on the foundational models. However, guardrails around usage, API access, and vector databases are for third-party use. 

2. AI Supply Chain Security

It’s more important than ever to vet vendors thoroughly before implementing AI systems. Practically, this means reviewing their compliance quarterly and stress-testing vendor systems for exploits. Also, prioritize managing packages, libraries, and data integrity to improve AI processes.

3. Complex Security Processes Around AI Architecture

Defenses need to evolve to anticipate these more nuanced attacks that target AI systems’ implementation and operational characteristics, not just the algorithms themselves.

4. Sophisticated in Evasion

With the rise of generative media, it’s essential to understand that generated images can fool computer vision models. Implement practices with “human-in-the-loop” systems to identify potential risks in this area. 

5. Proactive Testing

Invest in AI-specific testing to stay ahead of newer vulnerabilities that might come up. Training agents to do preliminary tests around chatbots will also help you become more secure. 

AI security and alignment are very active areas of research. A detailed testing process for any third-party AI vendor will help you make these decisions faster. We recommend choosing vendors with the certifications you need (Kommunicate, for example, has HIPAA, GDPR, SOC2, and ISO certifications) and then stress-testing them to identify potential vulnerabilities.

Conclusion

Security is no longer a “nice‑to‑have” for enterprise chatbots—it is the decisive factor determining whether customers will trust your AI and whether regulators will approve it. Organisations can protect sensitive data and unlock the operational gains promised by generative AI by building redaction directly into every conversational flow, enforcing least‑privilege access, and continuously auditing AI behaviour. Forward‑thinking CISOs treat these safeguards as a brand asset: they translate into faster user adoption, lower legal exposure, and a cleaner signal for future AI optimisation.

At Kommunicate, we hard‑wire these principles into our platform. Every deployment is backed by independent GDPR, HIPAA, SOC 2, and ISO/IEC 27001 certifications, and our real‑time PII redaction, granular role‑based access controls, and 24 × 7 security monitoring ensure that customer conversations remain compliant and confidential. 

As you plan your subsequent chatbot rollout, use security as the strategic lever it deserves to be. Align with vendors who can prove their controls, test them rigorously, and communicate those protections transparently to your end users. 

Need help in implementing a fast and secure AI chatbot for customer service? Talk to Us!