Rethinking AI safety: Why context matters in agent security

Security policies in AI systems are traditionally designed using pre-defined rules and user confirmations, which either over-restrict or under-protect agent actions. These rigid policies fail to address the nuanced nature of AI-assisted tasks, particularly in adversarial environments where context plays a crucial role in determining whether an action is harmful or benign.

Artificial intelligence (AI) agents are increasingly being deployed across diverse domains, from automating daily tasks to handling complex decision-making processes. However, as AI systems expand their capabilities, ensuring their security in a wide range of contexts has become a major challenge. Traditional security mechanisms, which rely on manually crafted policies or static access controls, often fail to account for the dynamic nature of AI interactions.

A recent study titled "Context is Key for Agent Security" by Lillian Tsai and Eugene Bagdasarian, published in arXiv (2025), presents an innovative approach to enhancing AI security by integrating contextual awareness. The research introduces Conseca, a framework designed to generate just-in-time, human-verifiable security policies that adapt to specific contexts, improving both usability and protection against security threats.

The need for contextual security in AI systems

Security policies in AI systems are traditionally designed using pre-defined rules and user confirmations, which either over-restrict or under-protect agent actions. These rigid policies fail to address the nuanced nature of AI-assisted tasks, particularly in adversarial environments where context plays a crucial role in determining whether an action is harmful or benign.

For instance, deleting an email may be a necessary action in one context but a security risk in another. Current security architectures either allow too much leeway, increasing exposure to vulnerabilities, or impose excessive restrictions, hindering AI efficiency. The study highlights how Conseca seeks to resolve these challenges by implementing context-aware security policies, ensuring that actions align with user expectations and are safeguarded against external manipulation.

Introducing Conseca: A context-aware security framework

Conseca is designed to create deterministic, just-in-time security policies that dynamically adapt to different contexts. Unlike conventional security frameworks, which rely on broad or manually-defined policies, Conseca generates security rules on demand using AI-powered analysis. It ensures that policies are fine-grained, taking into account factors such as user intentions, task requirements, and the surrounding environment.

A major innovation of Conseca is its ability to generate human-verifiable rationales for security policies. This allows developers and security experts to audit AI-generated policies, ensuring transparency and accountability. Additionally, by isolating security decisions from external influences, Conseca mitigates the risk of adversarial manipulation, such as prompt injection attacks that attempt to deceive AI agents into executing harmful actions.

Challenges and implementation considerations

While Conseca introduces a scalable approach to security, its implementation presents notable challenges. One of the primary concerns is ensuring that AI-generated security policies remain robust against adversarial inputs. The framework addresses this issue by isolating trusted contextual data from potentially malicious inputs, preventing attackers from altering security policies by manipulating contextual information.

Another key challenge is balancing security with usability. AI systems must maintain high levels of security without introducing excessive user friction. Over-reliance on manual user confirmations can lead to user fatigue, prompting individuals to approve security decisions without careful scrutiny. Conseca addresses this concern by leveraging AI to make automated, yet explainable, security decisions, reducing unnecessary interruptions while maintaining strict security measures.

Future of AI security: Towards scalable contextual defenses

Conseca marks an important step in context-aware security, but the study acknowledges that further research is needed to refine its approach. Future work should focus on expanding Conseca’s capabilities to additional AI-driven applications, from smart assistants to autonomous decision-making systems. By improving contextual understanding, AI security can move away from static policies toward more adaptive, intelligent protection mechanisms that align with user behavior and real-world constraints.

As AI agents continue to evolve, security strategies must evolve alongside them. Context-aware frameworks like Conseca provide a promising direction for ensuring that AI actions remain secure, explainable, and aligned with human intentions. By embedding context-driven policies into AI workflows, the industry can develop more resilient, scalable security architectures that protect against emerging cyber threats while enhancing AI usability and effectiveness.