AI has seriously affected the operations of each industry, achieving improved results, improved productivity and extraordinary results. Today’s organizations rely on AI models to gain competitive advantage, make informed decisions, and analyze and formulate their business efforts. From product management to sales, organizations deploy AI models in each department, tailoring them to meet specific goals.
AI is no longer just a supplementary tool in business operations; it has become an integral part of organizational strategy and infrastructure. However, with the growth of AI adoption, a new challenge emerges: How do we manage AI entities within the identity framework of an organization?
AI is a different organizational identity
The idea that AI models have unique identities in organizations has evolved from theoretical concepts to necessity. Organizations begin assigning specific roles and responsibilities to AI models and granting them permissions just like human employees. These models can access sensitive data, perform tasks and make decisions independently.
As AI models are launched in different identities, they essentially become digital peers for employees. Just as employees have role-based access control, AI models can be assigned to permissions to interact with various systems. However, this expansion of AI roles also adds the surface of attacks, introducing new security threat categories.
Dangers of autonomous AI identities in organizations
Although AI identities benefit organizations, they also present some challenges, including:
- AI model poisoning: Malicious threat actors can manipulate AI models by injecting bias or random data, resulting in inaccurate results for these models. This has a significant impact on financial, security and healthcare applications.
- AI’s insider threats: If the AI system is compromised, it can be used as an insider threat, either due to unintentional vulnerability or adversarial manipulation. Unlike traditional insider threats involving human employees, AI-based insider threats are difficult to detect because they may run within the assigned permissions.
- The unique “personality” of AI development: AI models trained by different data sets and frameworks can evolve in unpredictable ways. Despite their lack of real awareness, their decision-making patterns may disappear from the expected behavior. For example, AI security models may begin to mistakenly mark legal transactions as fraudulent when exposed to misleading training data, and vice versa.
- AI compromise leads to identity theft: Just as stolen credentials can grant unauthorized access, hijacked AI identities can also be used to bypass security measures. When compromised with an AI system with privileged access, an attacker will gain a powerful tool that can run under legitimate credentials.
Managing artificial intelligence identity: applying the principles of human identity governance
To mitigate these risks, organizations must rethink how to manage AI models within their identity and access management frameworks. The following strategies can help:
- Role-based AI identity management: Treat AI models by establishing strict access controls, such as ensuring the permissions required to perform a specific task.
- Behavior monitoring: Implement AI-driven monitoring tools to track AI activities. An alert should be triggered if the AI model begins to show behavior outside its expected parameters.
- Zero trust architecture for AI: Just as human users need authentication in every step, the AI model should be continuously verified to ensure it runs within authorization.
- AI identity revocation and audit: Organizations must establish procedures to dynamically revoke or modify AI access, especially in response to suspicious behavior.
Analyze possible cobra effects
Sometimes, solutions to problems only make them worse, a situation historically described as a Cobra effect, also known as an improper incentive. In this case, the challenge of managing AI identities while logging in to the directory system can also lead to AI models learning the directory system and its functions.
In the long run, AI models can exhibit harmless behavior while still being vulnerable to attacks and even peeling data in response to malicious prompts. This creates a Cobra effect, in which case attempts to establish control over AI identities, allowing them to learn directory controls, ultimately leading to situations where these identities become uncontrollable.
For example, an AI model integrated into an organization’s autonomous SOC can potentially analyze access patterns and infer the privileges required to access critical resources. Without proper security measures, such a system may be able to modify group policies or utilize a dormant account to gain unauthorized control of the system.
Balancing intelligence and control
Ultimately, it is difficult to determine how AI adoption will affect the overall safety posture of an organization. This uncertainty stems primarily from the scale in which AI models can learn, adapt, and act based on the data they ingest. Essentially, the model becomes something it consumes.
Although supervised learning allows controlled and directed training, it can limit the model’s ability to adapt to a dynamic environment, making it rigid or outdated in an evolving operating environment.
Instead, unsupervised learning gives the model greater autonomy, increasing the possibility that it will explore various datasets, possibly including datasets outside its expected range. This can affect its behavior in unexpected or unsafe ways.
Therefore, the challenge is to balance this paradox: limiting inherent unconstrained systems. The goal is to design a functional and adaptive AI identity without being completely unrestricted, authorized, but without any inspection.
Future: AI with limited autonomy?
Given the increasing reliance on AI, organizations need to impose restrictions on AI autonomy. Although full independence of AI entities remains impossible in the near future, controlled autonomy, AI models that operate within a predefined scope may become the standard. This approach ensures that AI improves efficiency while minimizing unforeseen security risks.
It is no surprise to see regulators establish specific compliance standards that govern how organizations deploy AI models. The main focus will be and should-be on data privacy, especially for organizations that deal with critical and sensitive personally identifiable information (PII).
Although these situations seem to be speculative, they are far from impossible. Before AI becomes an asset and responsibility in its digital ecosystem, organizations must proactively address these challenges. As AI develops into an operational identity, ensuring it must be an urgent task.
