# What is AI Safety?

_AI safety ensures AI systems behave predictably, align with human intent, and resist causing harm. Learn how Swept makes AI safety practical for enterprises._

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)**

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](/ai-prompt-injection), data leakage, fake outputs)
- **Degrade over time** due to [model drift](/ai-model-drift) or toxic feedback loops

AI doesn't need to be "sentient" to be dangerous. It just needs to be **unverified and [unsupervised](/ai-supervision)**.

## 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](/solutions/healthcare)

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

### [Fintech/Lending](/solutions/financial-services)

- 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](/product/evaluate)

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

### [Runtime guards](/product/supervise)

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](/ai-red-teaming) & feedback loops

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