Safety — Neuraphic

AI systems are becoming more capable at a rate that has surprised even the people building them. This trajectory creates extraordinary opportunities — and extraordinary risks. We believe companies developing this technology have an obligation to think clearly about those risks and to build systems that account for them from the ground up.

This document describes how Neuraphic thinks about AI safety: what concerns us, how we approach the problem, and what commitments we make.

Why safety matters now

The argument for taking safety seriously does not depend on any particular prediction about when artificial general intelligence will arrive. It depends on a simpler observation: AI systems are already being deployed in contexts where failures have real consequences — healthcare, finance, infrastructure, security — and the gap between what these systems can do and what we understand about how they work is growing.

Today's risks are not hypothetical. Prompt injection attacks can manipulate language models into ignoring their instructions. Adversarial inputs can cause classification systems to fail silently. Autonomous agents can take actions their operators did not intend. These are not future concerns. They are present realities, and they will become more severe as systems become more capable.

We build AI that operates in security-critical environments. If our systems fail, the consequences extend beyond our company to the organizations and infrastructure we are meant to protect. This is why safety is not a department at Neuraphic — it is an architectural constraint that shapes every system we build.

Safety as engineering, not compliance

There is a common pattern in the industry: build the system, then add safety measures on top. Fine-tune for harmlessness. Add content filters. Run a red-team exercise before launch. This approach treats safety as a quality assurance step — something that happens after the real work is done.

We take a different approach. Safety constraints are embedded in our systems at the architecture level. They are not prompt instructions that can be overridden. They are not post-hoc filters that can be bypassed. They are structural properties of how our systems process information, make decisions, and interact with the world.

This means accepting real trade-offs. Architecture-level safety constraints limit what our systems can do. They add complexity to our development process. They slow down certain capabilities in favor of reliability. We accept these trade-offs because systems that can be manipulated are not useful — they are liabilities.

What concerns us

We think about AI risk across three timescales:

Present risks. Adversarial attacks on deployed AI systems — prompt injection, jailbreaks, data poisoning, model manipulation. These attacks are well-documented, increasingly automated, and affect every organization deploying AI. Most companies are not adequately defended against them.

Near-term risks. Autonomous AI agents operating with insufficient oversight. As systems become more capable of independent action — executing code, making API calls, managing infrastructure — the consequences of misaligned behavior grow. An agent that misinterprets its objective can cause real damage before anyone notices.

Structural risks. The concentration of AI capability in organizations without adequate accountability structures. The systems being built today will shape the distribution of power for decades. If those systems are developed without transparency, without external oversight, and without mechanisms for course correction, the outcomes will reflect the incentives of the builders — not the interests of the people affected.

Our approach

We address these concerns through four overlapping strategies:

Adversarial research. We study how AI systems fail — not as a secondary concern, but as a primary research agenda. Our adversarial AI research focuses on understanding attack vectors, developing defenses, and building evaluation frameworks that keep pace with evolving threats. We publish this research openly because the industry improves when knowledge about vulnerabilities is shared responsibly.

Architecture-level constraints. Every system we build is designed with the assumption that it will be attacked, that it will encounter inputs its designers did not anticipate, and that it will need to fail safely when it does. Constraints are structural, not behavioral. They cannot be removed through clever prompting or adversarial input.

Evaluation before deployment. We do not deploy systems that we cannot evaluate. This means maintaining internal benchmarks for adversarial robustness, establishing clear capability thresholds that trigger additional review, and committing to pause deployment when evaluation results do not meet our standards. We have exercised this commitment, and we expect to exercise it again.

Transparency. We publish our safety framework, our responsible scaling policy, and our research findings. We believe that organizations building powerful AI systems owe the public a clear account of what they are building, why they are building it, and what risks they see. Transparency is not a virtue signal — it is an accountability mechanism.

Responsible scaling

As our systems become more capable, the bar for safe deployment rises. We have adopted a Responsible Scaling Policy that defines capability levels, evaluation criteria, and deployment conditions for each level. The core commitment is simple: we will not deploy a system at a given capability level until we are confident that our safety measures are sufficient for that level.

This includes the commitment to pause. If our evaluation framework indicates that a system exceeds our ability to deploy it safely, we will not deploy it — regardless of competitive pressure, business considerations, or the capabilities it demonstrates. We consider this commitment non-negotiable.

What we don't claim

We do not claim to have solved AI safety. No one has. The problems are deep, the technology is evolving rapidly, and honest researchers will acknowledge that many of the most important questions remain open.

What we do claim is that we take these problems seriously, that we have built our organization around addressing them, and that we hold ourselves accountable to a standard that we believe is appropriate for the technology we are developing. We invite scrutiny of these claims.

Responsible disclosure

If you discover a security vulnerability in any Neuraphic system, we want to hear about it. We take every report seriously and commit to acknowledging every report within three business days.

We will never take legal action against researchers who follow responsible disclosure practices. Security improves when researchers and companies work together.

Send findings to [email protected] with steps to reproduce. For AI model safety issues, contact [email protected].

Core views on AI safety