What is AI Safety?

AI safety is the discipline of ensuring AI systems behave in ways that are predictable, aligned with human intent, and resistant to causing harm. Whether by accident, design flaw, or emergent behavior.

Historically, "AI safety" referred to existential or long-term risks. Today, enterprises are applying it to real-world systems: LLM agents, copilots, classifiers, and automation pipelines that could misfire, mislead, or manipulate.

Swept makes AI safety practical. This includes risk scoring and validation to safety policies, escalation, and human override.

AI Safety vs AI Security vs AI Ethics

AI Safety

AI Safety is centered around preventing harmful behaviors. Example question to ask yourself: Will this model do something unsafe or unintended?

AI Security

AI Security prevents external manipulation. Can someone jailbreak your model or extract data?

AI Ethics

AI Ethics ensures fairness and values alignment. Does your agentic AI system reflect bias or violate norms?

Swept AI intersects all three. We enforce supervision, traceability, and control.

Where AI Safety Breaks Down

Without safeguards, autonomous or semi-autonomous AI can:

• Hallucinate facts in regulated industries (e.g., medical misdiagnosis, legal errors)
• Exploit reward functions (agents over-optimizing proxies, skipping steps)
• Accidentally cause harm via chain-of-thought, planning, or tool misuse
• Create security vulnerabilities (prompt injections, data leakage, fake outputs)
• Degrade over time due to model drift or toxic feedback loops

AI doesn't need to be "sentient" to be dangerous. It just needs to be unverified and unsupervised.

Swept's AI Safety Framework

We map safety into multiple operational layers. Each with tooling, metrics, and agents behind it:

Input & Prompt Safety

• Prompt filters
• Injection detection
• Context integrity validation
• Red-teaming agents

Model Output Safety

• Toxicity/bias checks
• Uncertainty estimation
• External fact validation
• Citation & trace auditing

Tool Use Safety

• Tool allowlists/denylists
• Sandbox execution
• Cost/rate-limiting policies
• Recursive function call guards

Behavioral Safety

• Plan reviews
• Simulation agents
• Self-reflection & contradiction spotting
• Safety-aware scaffolding

Organizational Safety

• Escalation rules
• Human-in-the-loop injection
• Audit trails and governance mapping
• Role-based oversight

AI Safety in the Age of Agentic Systems

Legacy AI safety focused on single predictions. But modern AI includes autonomous agents and multi-step planners using tools and APIs. That means:

• Safety has to be temporal (is the plan safe over time?)
• Safety has to be compositional (are toolchains reliable?)
• Safety has to be adaptive (does supervision adjust to risk?)

Swept AI's system aligns with enterprise safety policies, and enforces redlines before damage is done.

Some Real-World Use Cases

Digital Health AI

• Verifying claims summaries
• Preventing overconfident treatment recommendations
• Supervising patient-facing agents

Fintech/Lending

• Safe handling of financial data
• Avoiding hallucinated loan outcomes
• Flagging unsafe plan sequences in agent chains

Legal & Government

• Preventing unauthorized legal claims
• Protecting against prompt poisoning in public interfaces
• Ensuring all outputs cite real legal sources

Internal Automation

• Monitoring tool use (Slack, Notion, Jira)
• Preventing mass email sends or data wipes
• Applying safety budgets per action

How Swept Makes AI Safe by Default

Pre-deployment testing

Simulate agents in sandboxes. Stress-test risky inputs. Generate synthetic edge cases.

Runtime guards

Catch unsafe prompts, plans, or outputs before they go live.

Post-hoc reasoning

Trace agent behavior back through chain-of-thought, citations, and tool use.

Red-team & feedback loops

Inject adversarial tests. Adjust models and prompts based on results.