Anthropic Safeguards Framework for Claude

Anthropic employs a multi-layered safeguards strategy to ensure Claude's capabilities are used for beneficial outcomes while preventing real-world harm. This approach integrates experts in policy, enforcement, product, data science, threat intelligence, and engineering across the entire model lifecycle.

Policy Development and Harm Frameworks

Anthropic's Safeguards team designs the Usage Policy, which governs how Claude should and should not be used, specifically addressing child safety, election integrity, and cybersecurity. This policy development is guided by two primary mechanisms:

• Unified Harm Framework: A structured lens used to evaluate potentially harmful impacts across five dimensions: physical, psychological, economic, societal, and individual autonomy. This framework considers the likelihood and scale of misuse to inform enforcement procedures.
• Policy Vulnerability Testing: Anthropic partners with external domain experts in fields such as terrorism, radicalization, child safety, and mental health to stress-test policies. For example, during the 2024 U.S. election, a partnership with the Institute for Strategic Dialogue led to the implementation of a banner directing users to authoritative sources like TurboVote when seeking election information.

Integrating Safety into Model Training

Safeguards are integrated directly into the training process through collaboration between the Safeguards team and fine-tuning teams. This process involves:

• Behavioral Guidance: Defining specific traits and behaviors the model should exhibit or avoid during training.
• Iterative Refinement: Using evaluation and detection processes to identify harmful outputs, which then informs updates to reward models or adjustments to system prompts.
• Specialized Domain Expertise: Partnering with organizations like ThroughLine to refine Claude's responses to self-harm and mental health queries, ensuring the model provides nuanced support rather than simple refusals.

As a result, Claude is trained to decline assistance with illegal activities and recognize attempts to generate malicious code, fraudulent content, or plan harmful activities.

Pre-Deployment Testing and Evaluation

Before any new model is released, it undergoes three primary types of evaluations:

• Safety Evaluations: Testing adherence to the Usage Policy across various scenarios, including clear violations and multi-turn conversations. These are graded by other models with human review for accuracy.
• Risk Assessments: For high-risk domains—including cyber harm and chemical, biological, radiological, and nuclear weapons and high-yield explosives (CBRNE)—Anthropic conducts AI capability uplift testing with government and private industry partners.
• Bias Evaluations: Testing for political bias by comparing responses to opposing viewpoints and assessing them for factuality and consistency. The team also tests for bias related to identity attributes such as race, gender, and religion in contexts like healthcare and jobs.

For the "computer use" tool, pre-launch evaluations identified risks of augmented spam generation, leading to the development of new detection methods, prompt injection protections, and the ability to disable the tool for misusing accounts.

Real-Time Detection and Enforcement

Once deployed, Anthropic uses a combination of automated systems and human review to enforce the Usage Policy:

• Classifiers: Specially fine-tuned Claude models that monitor conversations in real-time for specific policy violations. These classifiers process trillions of tokens while minimizing compute overhead.
• CSAM Detection: Image hashes are compared against databases of known child sexual abuse material (CSAM) on first-party products.
• Enforcement Actions: These include "response steering," where system prompts are adjusted in real-time to prevent harmful output (or stopping the response entirely), and account-level actions such as warnings or termination.

Ongoing Monitoring and Threat Intelligence

Anthropic monitors traffic patterns to identify sophisticated attack patterns beyond individual prompts:

• Claude Insights: A privacy-preserving tool that groups conversations into topic clusters to analyze real-world use and inform guardrails.
• Hierarchical Summarization: A technique used to monitor computer use and cyber capabilities by condensing interactions into summaries to identify aggregate behaviors, such as automated influence operations.
• Threat Intelligence: The team monitors hacker forums, social media, and messaging platforms, and cross-references internal abuse indicators (like activity spikes) with external threat data to identify adversarial use.