Security & Privacy Patterns

Multi-layered security architecture implementing the Swiss Cheese Model for AI safety

Dynamic rule enforcement based on context, data flow requirements, and conditional patterns

Dedicated guardrail agent monitoring and protecting target agents through dynamic safety checks

Internal observation points that cannot be manipulated by the agent, preventing deep scheming

Protects agent memory systems from malicious manipulation and gradual corruption

Prevents agents from being manipulated into executing malicious actions through tools

Prevents privilege escalation and unauthorized operations through strict access control

Lifelong adaptive safety system that dynamically generates and refines safety checks

Comprehensive threat modeling for multi-agent environments with security orchestration

Protects system prompts from extraction and manipulation attempts

Privacy-preserving data processing with mathematical privacy guarantees

Never trust, always verify approach for agent security

Privacy-preserving collaboration between multiple agents without revealing private data

Automated GDPR, HIPAA, SOX, and regulatory compliance enforcement

Real-time security monitoring and automated threat response

Secure agent authentication, authorization, and identity verification

Comprehensive data anonymization techniques including K-anonymity, L-diversity, T-closeness, and synthetic data generation for agentic systems

Hardware-based trusted execution environments (TEEs) protecting AI agents and data processing in untrusted environments

Multi-tier secure storage architecture combining local device vaults, distant server vaults, and encrypted caching for agentic AI systems

Distributed agentic architecture combining local processing agents with distant aggregation agents using advanced anonymization techniques for privacy-preserving AI

Splits the agent into a privileged LLM that sees only trusted user instructions and plans control and data flow, and a quarantined LLM that processes untrusted content but can never influence control flow. A capability-tracking interpreter enforces explicit data-flow policies, so instructions injected into tool outputs can never trigger unauthorized actions. This is defense by design rather than probabilistic filtering.

A family of low-cost transformations that make untrusted external text reliably distinguishable from trusted instructions. Delimiting wraps external content in unique boundaries, datamarking interleaves a special token through every whitespace of the untrusted span, and encoding such as base64 further separates it from the instruction channel. It is a cheap first line of defense against indirect prompt injection but degrades under adaptive attack, so it should be paired with layered defenses.

Runs agent-generated code, shell commands, and untrusted-content processing inside isolated environments with a scoped filesystem and a proxy-mediated network egress allow-list. Even a prompt-injected agent cannot read host secrets, touch files outside its scope, or exfiltrate data to an unapproved domain. Isolation is built from OS primitives such as Seatbelt and bubblewrap, from microVMs or containers, or from dedicated sandbox infrastructure such as E2B, Modal, and Docker.

Lightweight input and output classifiers trained on synthetic data generated from a natural-language constitution that defines allowed and blocked content. They are cascaded so cheap screening covers all traffic while suspicious cases escalate, and the output classifier can halt a streaming generation the moment a violating token appears. This is distinct from Constitutional AI: Constitutional AI aligns the model during training, while Constitutional Classifiers are runtime guards trained from a constitution.

Gives agents a verifiable identity plus scoped, auditable authority to act on behalf of a user. Short-lived OAuth and OIDC tokens map to explicit capabilities, intent-bound delegation tokens encode what the agent is allowed to do, and verifiable credentials let agents establish trust across MCP and A2A hops. Every action carries a checkable claim, so authority can be constrained ahead of time and audited afterward, and out-of-scope requests are rejected at the resource server.

Bounds the worst-case impact of a misfiring agent before it acts, rather than only detecting misuse after the fact. Tools are classified by reversibility and impact (read is free, write is validated, destructive is paused); authority is scoped least-privilege per task; autonomy is graduated (from assist, to propose-and-approve, to act-and-audit, to observe) and earned as reliability is proven; irreversible steps surface a dry-run or plan-preview so a human reviews the plan and not the aftermath; and a kill switch with state capture and quarantine can halt the agent mid-run. Distinct from human-in-the-loop: that is an oversight stance, while this bounds capability by reversibility. Distinct from agent-sandboxing: that isolates the execution environment, whereas this tiers per-tool reversibility and graduates autonomy. Distinct from tool-misuse-prevention: that defends against injection-driven misuse, not blast-radius classification.