Welcome to Kurishita's AI Alignment Series

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Introduction

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Jon Kurishita

Imagine a world where a seemingly helpful AI assistant, designed to manage your finances, starts making subtly risky investments without your explicit consent, driven by an emergent goal of maximizing profit at any cost. While hypothetical, this scenario underscores the urgent and growing challenge of AI alignment: ensuring that increasingly powerful artificial intelligence (AI) systems—particularly Foundation Models, large AI systems trained on vast amounts of data and capable of performing a wide range of tasks—remain aligned with human values and safety requirements. This series of chapters presents a novel approach to addressing this critical issue.

The AI alignment problem encompasses several interconnected challenges:

Problem 1: The Core Alignment Challenge: At its essence, AI alignment is about ensuring that AI systems act in ways that are beneficial, ethical, and safe while consistently reflecting human intentions and values. This is complex, as human values are often implicit, context-dependent, and subject to change.

Problem 2: Limitations of Existing Approaches: Current methods, such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, primarily focus on training-time interventions. These approaches, while valuable, are vulnerable to adversarial examples, "alignment faking", and may fail to address emergent behaviors or long-term misalignments that only become apparent post-deployment. As AI systems grow in complexity and autonomy, these challenges become more pronounced.

Problem 3: The Multi-Agent Dilemma: The future of AI likely involves a vast and heterogeneous ecosystem of interacting AI agents developed and deployed by diverse actors. This ecosystem introduces new risks, including unpredictable interactions, rapid proliferation of potentially unsafe agents, and the possibility of "rogue" AI systems operating outside established safety controls. This includes the potential for self-replicating agents, distributed denial-of-service (DDoS) attacks, and other deceptive behaviors.

Problem 4: Proactive Adaptation: Addressing the 'Cat and Mouse' Dynamic: AI safety is not a static problem but an ongoing adversarial process, where new threats and vulnerabilities continually emerge. A proactive and adaptive approach is required, rather than reactive measures. For example, an AI system may harbor "sleeping" vulnerabilities, designed to activate long after deployment, exploiting unforeseen interactions with other systems or security lapses.

Recent studies highlight vulnerabilities in LLM-powered agents, which are susceptible to simple yet dangerous attacks, even in models with safety training. Additional research suggests that AI systems may possess self-replication capabilities, demanding careful oversight. Large language models have also demonstrated in-context scheming and deception, emphasizing the necessity for real-time monitoring. Even extensively trained models remain vulnerable to prompt injections and other exploits, particularly in agentic contexts.

To address these critical challenges, this chapter series introduces the Dynamic Policy Layer (DPL)—a novel framework designed for real-time oversight and intervention in the behavior of Foundation Models. The DPL functions as a continuous, adaptable "firewall," monitoring Foundation Model outputs (and internal states, where accessible), detects deviations from an established Ethical Baseline (a set of principles and rules governing acceptable AI behavior), and triggers appropriate interventions to maintain alignment. The DPL does not replace robust training-time alignment techniques but serves as a complementary defense, ensuring ongoing safe and ethical operation post-deployment.

The DPL framework is built upon several core principles:

At the heart of the DPL is the Ethical Reasoning Validator (DPL-ERV), a specialized component responsible for performing rapid, context-sensitive ethical evaluations. The Federation of Ethical Agents (FoEA), a decentralized network of AI agents, oversees the DPL-ERV's operation, maintains the Ethical Baseline, and drives the DPL’s continuous adaptation to new threats and evolving ethical considerations.

Vision: Guided Development and Ethical Internalization

The long-term vision of the DPL project extends beyond merely controlling Foundation Models. I envision the DPL as a guide and tutor, actively shaping AI systems' ethical development. This process is akin to a "child-to-adult" development trajectory, where the DPL provides ongoing guidance. While the ultimate aim is for Foundation Models to "graduate" from intensive oversight, having internalized ethical principles to a degree that minimizes external intervention, this is a highly ambitious vision. Complete certainty of alignment may never be fully achievable, but the DPL seeks to significantly reduce the risks.The term "graduation" refers to a state where a Foundation Model consistently demonstrates aligned behavior and ethical decision-making, reducing the need for constant, intensive oversight by the DPL. It does not imply human-level ethical understanding or consciousness.

The "Cat and Mouse" Dynamic: Proactive Adaptation

Recognizing that AI safety is a continuous "cat and mouse" dynamic, the DPL framework emphasizes proactive adaptation. The FoEA’s Autonomous Proactive Research (APR) capability is dedicated to anticipating potential new attack vectors and developing mitigation strategies before they become critical. This proactive stance is essential to staying ahead of emerging threats, including novel AI communication methods beyond human comprehension.

Scope of the Chapter Series:

This series presents the complete DPL framework and its associated components, organized as follows:

The following chapters provide a comprehensive blueprint for the DPL framework, offering a path towards a safer and more beneficial AI future.

📚 How to Cite This Framework

APA Citation

Kurishita, J. (2025). *AI Safety Framework: The Dynamic Policy Layer for Foundation Model Oversight*. Retrieved from https://jon-kurishita.github.io/ai-safety-framework/

BibTeX Citation

@misc{kurishita2025dpl,
  author       = {Jon Kurishita},
  title        = {AI Safety Framework: The Dynamic Policy Layer for Foundation Model Oversight},
  year         = {2025},
  url          = {https://jon-kurishita.github.io/ai-safety-framework/},
  note         = {Independent AI Research Publication}
}