TL;DR
AI isn’t only changing how we work — it’s transforming the way we secure, manage, and monitor entire systems.
As AI adoption continues to expand, the limitations of traditional approaches to security are increasingly becoming more apparent. Because they’re designed to protect static systems and largely predictable threats, conventional approaches cannot keep up with the dynamic, evolving nature of AI. As a result, many organizations are now caught off guard by the security vulnerabilities and risks that threaten them.
Clearly, in our AI era, a different approach to security has become necessary. As Raghu Bala explains it in our new AI Security Lifecycle course, organizations now need to start thinking about how they can build end-to-end security frameworks flexible enough to safeguard both AI models and the data they use.
Why AI security threats are so different
Learning how to properly secure AI systems starts with understanding how differently they store and manage data from traditional systems.
“In a regular SaaS system,” Bala explains, “information is held in a document or relational database, where you can make a clear distinction between different company data. But in an AI system, the underlying database is a vector database. There is no segregation — so whatever you give to this model, it's all jumbled up together and your information could be out of your control.”
This isn’t a problem if you’re running your own AI models on your own infrastructure, but most organizations use LLMs hosted by companies like OpenAI or Google. This introduces the possibility of security risks, such as unauthorized data access and information leaks. Savvy attackers can even use techniques such as data poisoning to manipulate outputs.
“AI is heavily data dependent,” says Bala. “Threats can often come in from the data layer. All it takes is for someone to inject incorrect information into the database, which can manifest itself in results later on.”
These vulnerabilities increase further when the data or tools the AI systems have access to is poorly regulated, or when unauthorized AI agents, often referred to as “shadow AI,” get used within your network. While some of this can be mitigated through role-based access controls and continuous fine-tuning, the constantly shifting nature of AI systems means that, without a more comprehensive framework in place, these vulnerabilities will persist.
Building an end-to-end AI security lifecycle
In order to create secure AI models and properly protect the data that these models employ, Bala recommends taking a layered approach. This will make it easier to address threats and vulnerabilities wherever they may be in your stack, from your raw data to your model interface.
“The first thing you should establish are your cybersecurity fundamentals,” says Bala. These are the defenses and best practices, like access controls and authentication, that should be common to all systems. “Then you need to focus on ensuring your data does not leave your premises, which you can do by running private models and continuously fine-tuning them so that the data remains accurate.”
For organizations using AI to run tools, such as coding agents, the next step Bala recommends is to put in place oversight practices to ensure accuracy. “If you want to deploy AI-generated code, you have to really understand how it works. If that code has vulnerabilities, it can potentially expose your entire organization.” This means designing workflows that put humans in the loop so that they can evaluate, certify, or reject AI outputs as needed.
It’s also vital to continuously test the models themselves for accuracy. Bala recommends using length-based accuracy tests, which evaluate how well a model performs when given complex, multi-step tasks. If model accuracy is dropping, then this will tell the organization that they will need to fine-tune their system once again. Otherwise, any errors in the AI output may threaten organizational security.
Bala also mentions the importance of establishing guardrails for AI systems — or constraints that you set around the model that limit what it can do or say. Not only are guardrails important for preventing basic security breaches, such as a prompt asking the AI to reveal everyone’s passwords, but they also help models avoid generating harmful or unsafe content that could put an organization at risk.
“You want the AI to be able to explain cybersecurity, but not go so far as to explain how to breach a system,” says Bala. “Guardrails help you prevent this.”
Putting your AI security to the test
Establishing secure architectures, implementing access controls, and building strong governance and management frameworks around your AI system are all important — but how do you actually know if any of these measures are working? Similar to traditional cybersecurity, you must put it all to the test.
Except while conventional security practices have concepts like red teaming and ethical hacking, AI security uses means such as adversarial testing and bias assessment.
“For adversarial testing,” says Bala, “it’s almost like we’re role playing. We want to put the model under pressure to see if it can handle an edge-case scenario. We have to challenge it.” For example, this could mean subtly altering a segment of malware code to see if the AI is still able to identify it. Or it could mean intentionally injecting malicious data into the model’s training dataset, then seeing if that alters its behavior. “We want to see if the AI is still able to defend itself,” says Bala.
Bias assessment is different. Rather than trying to exploit vulnerabilities, this strategy’s goal is to uncover an unfair of discriminatory patterns embedded in the AI. “Consider the data coming out of public models, like OpenAI and Gemini,” says Bala. “Its primary source is North American data. So if I ask about crop disease, for example, but live in Africa, the AI’s output may not be applicable to me.” This can raise security concerns when this tendency toward bias leads to false positives that incorrectly identify threats, or false negatives that miss them altogether.
For Bala, all this just underlines the most important aspect of AI security: the need for continuous human oversight. “AI is a constantly changing environment,” he says. “This means that a lot of the things we’re talking about are not one-time activities. AI systems are constantly learning and changing. Your threat modeling might handle today's problem, but if the system’s knowledge base is changing, then whatever threats you thought were applicable yesterday may not be applicable tomorrow.”
Make safety and security an ongoing aspect of your AI lifecycle
AI systems are fundamentally different from the traditional network environments that preceded them. Because of this, they require a fundamentally different approach to security. As AI usage continues to expand, so will the threats that these systems face — which makes it more important than ever to start learning how to establish a comprehensive security lifecycle.
From planning and data preparation to deployment, monitoring, and governance, begin discovering the different stages of AI security in our AI Security Lifecycle course.
