When insurers start introducing agentic AI workflows (that is, AI systems that can act semi-independently) they unlock major gains in efficiency. However, they also introduce new risks, particularly regarding the storage, access, and processing of customer and transactional data. If these systems aren’t secure by design, they can become targets for breaches and leaks.
So, how can insurers leverage the benefits of AI while maintaining data protection? Let’s look at the core strategies.
1. Start with Zero Trust Architecture
Zero Trust is no longer optional. In AI-driven environments, especially where sensitive financial and health data is involved, trust can’t be assumed based on user location or credentials. Instead, every user, device, and application must be continuously verified.
Implementing Zero Trust starts with identity and access management (IAM) controls, multi-factor authentication, and least-privilege policies. But it doesn’t stop there. AI workflows should be designed so that data is only exposed to the components that need it, and only for as long as necessary.
This means that when you're deciding which tool-calling freedom your agentic AI has access to, you need to be cautious. For example, suppose an AI is allowed to pull customer profiles to support a claims process.
In that case, it should also not be able to access payment systems, underwriting models, or unrelated customer segments simultaneously. Each capability should be isolated and independently permissioned. If one action is compromised or misused, it shouldn't open the door to everything else.
2. Secure Data at Every Touchpoint
AI systems often gather data from multiple sources and process it across various environments, including on-premises servers, cloud platforms, and third-party APIs. That data must be secured at every point: at rest, in transit, and during processing.
This involves utilising robust encryption standards, such as AES-256 and TLS 1.3, isolating data pipelines, and managing where models execute and store intermediate data. It also includes data tokenization or pseudonymization in training environments, especially when using real customer data. This helps reduce exposure while still supporting model accuracy.
3. Protect the Model, Too
It’s easy to focus only on the data, but in insurance, the AI model itself can be a target. Attackers might try to reverse-engineer it, extract sensitive training data, or feed it malicious inputs to manipulate results.
This is where techniques such as adversarial testing, input validation, and model watermarking come into play. Insurers should run simulations to stress-test models under abnormal conditions and flag vulnerabilities.
Access to models should be logged, rate-limited, and controlled with as much care as access to raw data.
4. Monitor What AI Is Doing, And Why
One of the trickiest things about AI in insurance is explainability. You need to know why a model flagged a claim as fraudulent or suggested a particular risk score. That’s not just a regulatory requirement; it’s also crucial for catching errors and abuse.
Tools that offer explainable AI (XAI) capabilities are handy in this context. They help insurers understand decision paths, highlight potential bias, and improve transparency across departments.
This clarity also makes it easier to spot when something goes wrong, such as when a model starts making out-of-character recommendations based on poor training data or external tampering.
5. Segment and Audit Every Workflow
Insurance data often spans multiple systems, including claims processing, underwriting, fraud detection, and customer service. When AI impacts any of these areas, it’s essential to segment each workflow and log activity.
Automated logging and auditing should be implemented across all systems. That includes input and output data, model versions, user access, and even rejected or flagged data points. If something goes wrong, a strong audit trail is the first step in understanding what happened and proving compliance. Segmentation also limits the blast radius of any breach. If one part of the system is compromised, others can be isolated quickly.
This is also where there’s a major opportunity to bring in Camunda as a cross-platform process orchestration and automation layer. Camunda specializes in stitching together workflows across disparate systems while enabling full visibility and control. They're a core partner in this space, and Agentic is a key part of that push, helping to ensure AI-driven processes are both traceable and governable from end to end.
6. Regularly Validate Your AI Use Cases
AI is not “set and forget.” Every new use case, from customer onboarding to claims automation, needs its threat model and risk review. If you're using AI to streamline back-office operations, boost decision-making or improve underwriting accuracy, those gains are real, but they must be paired with equally strong safeguards.
Even well-designed systems can become vulnerable over time. Regular threat assessments, red-teaming exercises, and compliance reviews help ensure your AI doesn’t become a weak link.
AI Brings Speed and Smarts- But It Needs Guardrails
If you’re building agentic workflows or scaling AI across your operations, it helps to have the right guidance.
At BP3, we collaborate with insurance providers to design secure and efficient systems that protect customer data while unlocking the full potential of automation. Our consulting services and AI solutions are designed to help teams move quickly without compromising control.
We’ve seen firsthand how AI can drive efficiency in insurance, streamline decision-making and reduce operational drag. But none of that matters if the foundation isn’t secure. Getting that part right, at the core, is what enables innovation you can trust.