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AI Agent Safety for Cybersecurity Operations
Safety controls for AI agents performing security operations including threat hunting, incident response, and vulnerability management.
Guides on agent safety, prompt injection, MCP security, compliance, and building safe AI systems.
Safety controls for AI agents performing security operations including threat hunting, incident response, and vulnerability management.
Safety controls for AI agents creating, editing, and distributing content in media and publishing organizations.
Build an automated red team harness that tests your safety policies against a library of attack scenarios before deploying to production.
Safety controls for AI agents managing supply chains, fleet operations, and warehouse automation.
Safety controls for AI agents in manufacturing environments covering production systems, quality control, and industrial safety.
Safety controls for AI agents operating in government contexts with requirements for transparency, accountability, and FedRAMP compliance.
Safety requirements for AI agents handling insurance underwriting, claims processing, and policyholder interactions.
Safety controls for AI agents in real estate covering fair housing compliance, transaction safety, and document handling.
A comprehensive guide to designing an enterprise AI safety program covering governance, technical controls, operations, and compliance.
A curated list of free and open-source tools for adding safety controls to AI agents, from policy enforcement to prompt injection detection.
Safety patterns for AI customer support agents that handle sensitive conversations, account actions, and escalations.
Safety requirements for AI agents serving students, educators, and educational institutions.
Safety controls for AI agents handling recruitment, employee data, and HR processes.
Safety controls for AI agents that manage infrastructure, deployments, and operational tasks in production environments.
Safety patterns for AI agents that manage product listings, process orders, and interact with ecommerce customers.
Safety controls for AI agents handling legal documents, client communications, and case management.
A comparison of monitoring tools for AI agents in 2026, covering behavioral monitoring, observability, and safety-specific monitoring.
AI agents can read files, call APIs, run shell commands, and send emails. Agent safety is the practice of controlling what they are allowed to do before they do it.
Safety requirements and implementation patterns for AI agents operating in financial technology environments.
Implementing safety controls for AI agents that handle patient data, clinical workflows, and healthcare operations.
How to build an Authensor adapter for any AI agent framework, wrapping tool calls with policy enforcement and audit logging.
A quarterly roadmap template for planning and tracking AI safety initiatives across an organization.
How malicious data from one agent session can contaminate other agents through shared state, memory, and communication channels.
A comparison of open-source tools for AI agent safety in 2026, covering Authensor, NeMo Guardrails, Guardrails AI, LlamaGuard, and others.
Prompt injection is the most common attack against AI agents. It works by embedding instructions in data that the agent processes, causing it to ignore its original instructions and follow the attacker's instead.
Common ways AI agents inadvertently leak API keys and the controls that prevent credential exposure.
Add custom behavioral monitors to Sentinel for tracking domain-specific metrics like spending patterns, data access volumes, and communication frequency.
Output filtering scans an AI agent's responses before they reach users or external systems, catching information leaks, PII exposure, and harmful content.
How attackers can exploit WebSocket connections in AI agent architectures to inject commands or exfiltrate data.
A five-level maturity model for assessing and improving an organization's AI agent safety practices.
How markdown image tags in AI outputs can exfiltrate data through URL parameters when rendered in a browser.
The Model Context Protocol gives AI agents access to tools. Without authorization controls, any connected agent can call any tool with any arguments. Here is how to secure MCP servers.
Startups building AI agents need safety tools that are free to start, quick to deploy, and scale with the product. Here is what to use.
Using canary deployment patterns to safely roll out updates to AI safety infrastructure.
How AI agents can be exploited for DNS-based data exfiltration and the network controls that prevent it.
Extend Aegis with custom detection rules for domain-specific threats that the default detectors do not cover.
How compromised or manipulated AI agents can steal credentials and how to prevent credential exposure in agent systems.
Validate every argument an AI agent passes to a tool: type checking, range validation, path traversal prevention, and injection defense.
How to replace homegrown AI safety checks with Authensor's structured policy engine, audit trail, and monitoring stack.
How path traversal attacks exploit AI agent file access tools to read or write files outside authorized directories.
A practical guide to migrating from NVIDIA NeMo Guardrails to Authensor, covering concept mapping and policy translation.
A survey of regulatory requirements that apply to AI systems with autonomous decision-making capabilities, across jurisdictions and frameworks.
How server-side request forgery attacks exploit AI agent network access to reach internal services.
A decision framework for choosing between building custom AI safety infrastructure and adopting an existing tool like Authensor.
How attackers use AI agents to execute system commands and how to prevent command injection in agent tool chains.
A structured framework for evaluating and comparing AI safety tool vendors across technical, operational, and business dimensions.
How to plan and justify budget for AI safety infrastructure, covering tooling, staffing, testing, and compliance costs.
How AI agents can be manipulated into executing SQL injection attacks through their database tools.
Implementing health checks that verify AI agents are functioning correctly and safely.
How to structure an AI safety function within an engineering organization, including key roles and responsibilities.
Transparency requirements demand that AI agent systems explain what they do, how they make decisions, and what actions they have taken.
How to build a compelling business case for AI safety investment that resonates with engineering leadership, legal, and executives.
Restricting and monitoring AI agent file system access to prevent data theft, corruption, and unauthorized modifications.
Design principles behind Authensor's YAML policy schema: readability, composability, and safe defaults.
How to quantify the return on investment of AI safety infrastructure using incident cost avoidance, compliance savings, and operational metrics.
A structured timeline for rolling out AI safety controls across an organization, from pilot to full enforcement.
MCP servers are a supply chain dependency for AI agents. Securing them requires verification, monitoring, and isolation.
Comprehensive safety controls for AI agents that access the web, from URL filtering to response scanning.
Strategies for adding safety controls to AI agents already running in production without causing downtime or behavioral regressions.
How to integrate Authensor safety controls into an existing LangChain or LangGraph agent with minimal code changes.
How to sandbox code execution in AI agents to prevent file system damage, network abuse, and resource exhaustion.
How to set retention periods for AI agent audit logs based on regulatory requirements and operational needs.
A phased approach to adding Authensor safety controls to AI agents that currently operate without guardrails.
Treating AI safety policies as code enables version control, peer review, testing, and automated deployment of governance rules.
Safety architecture for AI agents that control computer interfaces through mouse clicks, keyboard input, and screen reading.
Strategies for collecting, storing, and querying logs from distributed multi-agent AI systems.
Techniques for analyzing audit logs and monitoring data to investigate AI agent safety incidents effectively.
Red teaming tests your AI agent's defenses by simulating real attacks, from prompt injection to multi-step exploitation chains.
A guide to diagnosing and fixing issues with Sentinel's behavioral anomaly detection, including threshold tuning and false alerts.
Safety controls for AI agents that browse the web, covering navigation restrictions, data handling, and injection prevention.
How to prevent and diagnose memory leaks in long-running AI safety monitoring processes.
Comparing self-hosted open-source AI safety tools with managed safety services, covering control, cost, compliance, and operational requirements.
A practical checklist for preparing your AI agent deployment for compliance audits, covering access controls, logging, monitoring, and documentation.
Techniques for reducing the latency impact of safety checks without compromising the effectiveness of your guardrails.
Applying safety constraints to DSPy programs where prompts are optimized automatically rather than written by hand.
How to diagnose, document, and recover from hash chain integrity breaks in cryptographic audit trails.
System prompts are suggestions to the model. Policy engines are enforcement in code. Understanding the difference is critical for production safety.
How Sentinel uses EWMA and CUSUM statistical algorithms to detect behavioral anomalies in AI agent action patterns.
How to identify and resolve situations where approval workflows stall, preventing agents from completing tasks.
A troubleshooting guide for resolving connection failures between AI agents, MCP gateways, and downstream MCP servers.
Integrating safety checks into Haystack document processing and question-answering pipelines.
Penetration testing for AI agents covers prompt injection, policy bypass, privilege escalation, and data exfiltration beyond traditional infrastructure testing.
The most frequent configuration errors when setting up Authensor, with explanations of why they happen and how to fix them.
Implementing distributed tracing to follow AI agent actions across services and components.
ISO 42001 is the first international standard for AI management systems. Here is how it applies to organizations deploying AI agents.
Adding Authensor safety controls to Microsoft Semantic Kernel agent pipelines and plugin execution.
A systematic guide to diagnosing and fixing policy evaluation failures in Authensor's policy engine.
A technical deep dive into how hash-chained receipts work, including the hashing algorithm, chain construction, and verification process.
How to identify, diagnose, and resolve false positive detections in content safety scanners without weakening real protections.
Applying causal inference methods to determine what caused AI safety incidents rather than just what correlated with them.
Adding safety checks to LlamaIndex RAG pipelines to prevent data exfiltration and poisoned retrieval results.
Use hash-chained receipt logs and behavioral data to reconstruct exactly what an AI agent did during an incident.
Comparing Authensor's open-source agent safety stack with AWS Bedrock Guardrails' managed content filtering service.
How to apply NIST's AI Risk Management Framework to AI agent deployments, mapping its four functions to concrete safety controls.
Understanding and managing the tradeoffs between AI agent performance and safety enforcement overhead.
Why Authensor's core packages have zero runtime dependencies, and how this design choice improves security, reliability, and portability.
Comparing Authensor and Guardrails AI: deterministic policy enforcement vs LLM output validation for AI agent safety.
How to respond when an AI agent causes harm or is compromised, from immediate containment through root cause analysis and remediation.
A structured template for conducting blameless post-incident reviews after AI agent safety incidents.
A side-by-side comparison of the EU AI Act, NIST AI RMF, ISO 42001, and other governance frameworks relevant to AI agent deployments.
Configuring safety controls for Microsoft AutoGen multi-agent conversations and tool execution.
Using Bayesian methods to quantify and update AI safety risk estimates as new evidence arrives.
Comparing direct MCP connections with an MCP gateway for security, observability, and control over AI agent tool access.
Practical techniques for reducing false positives in AI safety monitoring without compromising detection capability.
How to deploy AI agents in healthcare settings while meeting HIPAA requirements for access controls, audit trails, and data protection.
A template for conducting structured safety reviews of AI agent deployments, covering risk assessment, control validation, and sign-off.
A technical comparison of Authensor and NVIDIA NeMo Guardrails for AI agent safety, covering architecture, enforcement model, and deployment.
Adding safety enforcement to Claude-powered agents using Authensor's MCP gateway and SDK integration.
Implement tenant isolation for AI agents: separate policies, separate audit trails, and cross-tenant access prevention.
Anomaly detection identifies when an AI agent's behavior deviates from its baseline, catching attacks and failures that rule-based checks miss.
How to meet SOC 2 Trust Services Criteria when deploying AI agents, covering access controls, logging, monitoring, and change management.
Strategies for reducing alert fatigue so that critical AI safety alerts are noticed and acted upon.
Budget controls limit the financial and computational resources an AI agent can consume, preventing runaway costs and uncontrolled spending.
A checklist for evaluating the security and reliability of third-party MCP servers before connecting them to your agents.
Implementing safety checks in Vercel AI SDK applications with streaming-compatible patterns.
Cross-agent tracing links the actions of multiple AI agents in a workflow into a single trace, enabling end-to-end visibility and forensic analysis.
Rate limiting prevents AI agents from executing too many actions in a short period, defending against runaway loops, data exfiltration, and resource exhaustion.
Design patterns for approval workflows that keep humans in the loop without creating bottlenecks or alert fatigue.
Building behavioral profiles that define normal agent operation and enable detection of deviations.
Session risk scoring assigns a dynamic risk level to an AI agent session based on its actions, enabling adaptive safety policies.
A checklist for safely onboarding new AI agents into an organization's infrastructure with proper controls from day one.
A kill switch immediately terminates an AI agent session when it detects dangerous behavior or when an operator intervenes.
Adding safety guardrails to CrewAI multi-agent crews using Authensor's CrewAI adapter.
Applying statistical rigor to AI safety benchmarks to avoid drawing incorrect conclusions from noisy data.
Principal binding ties an AI agent's actions to a specific identity, so every action is attributed and accountability is maintained.
High-risk AI systems must comply with the EU AI Act by August 2026. Here is a practical timeline for getting AI agent systems ready.
Techniques for detecting anomalous AI agent behavior in real time before it causes harm.
Shadow evaluation runs a new policy alongside the active one without enforcing it, letting you test policy changes against real traffic before deploying.
Use event-driven architecture to monitor AI agent behavior, route alerts, and trigger automated responses without adding latency to agent operations.
A template of alert rules for AI agent monitoring covering anomaly detection, error rates, and behavioral drift.
Integrating Authensor with the OpenAI Agents SDK to add policy enforcement and audit trails to OpenAI-powered agents.
Ensuring that AI safety evaluations produce consistent, reproducible results across runs and environments.
When AI agents communicate with each other, every message is a potential injection vector. Secure inter-agent communication with scanning, authentication, and scope isolation.
Alignment trains models to want the right things. Guardrails prevent wrong things from happening regardless of what the model wants.
How to add Authensor safety checks to LangChain and LangGraph agent pipelines using the official adapter.
Establishing and verifying the provenance of AI models used in agent systems for security and compliance.
Deterministic safety uses code-based rules that always produce the same result. Probabilistic safety uses the model itself, which can be inconsistent and manipulated.
How to manage API keys, tokens, and secrets for AI agent deployments, covering scoping, rotation, storage, and monitoring.
Pre-built policy templates that map regulatory requirements to enforceable AI agent safety rules.
Deploying AI safety components as microservices enables independent scaling, language-agnostic integration, and clear separation of concerns.
A tiered approval workflow template for AI agents that handle financial transactions, with escalation paths and audit requirements.
Drift detection identifies when an AI agent's behavior gradually changes from its established baseline, catching problems that sudden anomaly detection misses.
A step-by-step guide to running the full Authensor stack locally with Docker Compose for development and testing.
Protecting the integrity of AI model weights from training through deployment to prevent supply chain attacks.
Set up Sentinel to track AI agent behavior patterns, detect anomalies, and alert on drift from established baselines.
Human-in-the-loop AI is a design pattern where AI agents pause at critical decision points and require human review before proceeding.
Using incident data to automatically generate or suggest policy rules that prevent recurrence.
Network egress controls restrict which external destinations an AI agent can communicate with, preventing data exfiltration and command-and-control connections.
Configure Aegis detection rules, thresholds, and custom patterns to scan AI agent inputs for prompt injection, PII, and credential exposure.
A YAML policy template for data analysis agents with query restrictions, export controls, and PII handling rules.
Article 15 requires high-risk AI systems to be resilient against unauthorized manipulation, including prompt injection and other adversarial attacks.
Using Terraform to provision and manage the infrastructure required for production AI safety deployments.
Using zero-knowledge proofs to demonstrate AI compliance without revealing sensitive system details.
A cascading failure occurs when one compromised or malfunctioning AI agent causes failures in other agents it communicates with, amplifying the impact.
API reference and usage guide for the Authensor Python SDK, covering guard creation, policy loading, and framework integration.
Establishing regular policy audit and review processes to keep AI safety policies effective and current.
Memory poisoning is an attack where adversaries inject false or malicious information into an AI agent's memory or context to influence future decisions.
A YAML policy template for AI coding assistants with file system boundaries, execution limits, and secret protection.
Setting up GitHub Actions workflows to validate safety policies and scan for vulnerabilities in AI agent repositories.
How homomorphic encryption enables computation on encrypted AI agent data without decryption.
Privilege escalation occurs when an AI agent gains access to tools or data beyond its authorization. Prevent it with deny-by-default policies and strict scope boundaries.
API reference and usage guide for the Authensor TypeScript SDK, covering guard creation, policy loading, and receipt management.
Article 14 requires that AI systems can be effectively overseen by humans, including the ability to understand outputs and intervene when needed.
Designing break-glass mechanisms that allow emergency policy overrides while maintaining accountability.
Identity and privilege abuse occurs when an AI agent operates with more permissions than necessary or impersonates users to access restricted resources.
Complete reference for the Authensor CLI tool, covering policy management, receipt queries, and control plane operations.
A YAML policy template designed for customer-facing AI agents handling support tickets, refunds, and account inquiries.
Adding automated safety validation to your CI/CD pipeline so policy changes and agent updates are verified before deployment.
Applying differential privacy to AI agent logs to protect individual user data while preserving analytical utility.
Tool misuse occurs when an AI agent uses a legitimate tool in an unintended or harmful way, such as reading sensitive files through a general file-read tool.
Comparing default-deny and default-allow policy strategies and their implications for AI agent safety.
Deploy the Authensor control plane, PostgreSQL, and monitoring stack using Docker Compose.
Safety checks at different points in the agent pipeline catch different threats. Place them at input, pre-execution, post-execution, and output stages.
A permissive but instrumented YAML policy template for development and testing environments.
Using federated learning to improve AI safety models across organizations without sharing sensitive data.
Which Prometheus metrics to instrument for AI safety systems and how to use them for alerting and capacity planning.
Goal hijacking occurs when an attacker redirects an AI agent from its intended objective to a malicious one, typically through prompt injection or context manipulation.
Use Authensor's MCP gateway to enforce safety policies on Claude Code's tool calls, including file access and shell commands.
How to handle conflicting rules when multiple policies apply to the same AI agent action.
The OWASP Agentic Top 10 is a catalog of the most critical security risks for AI agent applications, from prompt injection to cascading failures.
A production-ready YAML policy template with strict defaults, tool allowlists, and approval requirements for high-risk actions.
Building Grafana dashboards to visualize AI agent safety metrics, policy evaluations, and anomaly detection.
Data exfiltration occurs when an AI agent sends sensitive data to unauthorized destinations. Prevent it with egress controls, output scanning, and least-privilege policies.
Applying transfer learning to build effective safety detection models with limited labeled data.
Add safety guardrails to CrewAI agents and crews using the Authensor adapter for multi-agent workflows.
Article 12 requires automatic logging of all significant events during AI system operation, with tamper-resistant storage for at least six months.
Strategies for incrementally deploying policy changes to minimize risk and catch issues early.
Tool authorization controls which tools an AI agent can call and under what conditions, enforced at runtime by a policy engine.
A practical checklist mapping EU AI Act requirements to specific controls for autonomous AI agent deployments.
Add policy enforcement and content scanning to OpenAI Agents SDK tool calls using the Authensor adapter.
Understanding and improving the robustness of safety classifiers against adversarial evasion attacks.
How to configure webhook notifications for safety events so your team responds to incidents in real time.
An AI agent firewall inspects and controls all actions an agent takes, blocking unauthorized operations before they execute.
MCP tool descriptions are sent to the language model and can be exploited for prompt injection. Here is how to validate and secure them.
How to safely compare two policy versions in production to measure their impact on safety and usability.
Integrate Authensor's safety layer into a LangChain or LangGraph agent using the official adapter.
Article 9 requires a risk management system that identifies and mitigates risks throughout an AI agent's lifecycle, from design through deployment.
A step-by-step checklist for responding to AI agent safety incidents, from detection through resolution and post-incident review.
Using information theory to detect prompt injection by measuring statistical properties of input text.
Using Redis to cache policy definitions and evaluation results for faster AI safety checks.
Prompt injection defense is the set of techniques used to prevent attackers from overriding an AI agent's instructions through manipulated input.
Generate tamper-evident, hash-chained audit logs for every action your AI agent takes, from tool calls to policy decisions.
The EU AI Act imposes specific requirements on AI systems with agentic capabilities, including risk management, human oversight, logging, and cybersecurity.
Managing policy versions and safely rolling back to previous policy configurations when issues arise.
Fail-closed means that when something goes wrong or no rule matches, the system denies the action rather than allowing it.
A structured checklist for auditing the security posture of MCP servers before connecting them to AI agents.
Using game theory to model interactions between AI agents, attackers, and safety systems.
Why PostgreSQL is a strong choice for AI agent logging and how to configure it for safety audit workloads.
Set up human-in-the-loop approval workflows so your AI agent pauses and waits for human review before executing high-risk actions.
Security best practices for building and deploying MCP servers, covering authentication, input validation, tool description safety, and transport security.
Using attribute-based access control to create fine-grained, context-aware policies for AI agents.
Behavioral monitoring tracks AI agent actions over time and detects anomalies that single-action rules cannot catch.
A pre-deployment security checklist covering policy configuration, access controls, monitoring, and incident response for AI agents.
Use Aegis to detect prompt injection attempts in tool arguments, user messages, and retrieved documents before your agent processes them.
How to design a database schema for hash-chained audit receipts that are tamper-evident and query-efficient.
Applying formal verification techniques to mathematically prove properties of AI safety policies.
A hash-chained audit trail links each log entry to the previous one using cryptographic hashes, making it impossible to alter or delete records without detection.
Common architecture patterns for adding safety layers to AI agent systems, from embedded guards to centralized gateways.
Deploy an MCP gateway that enforces safety policies on every tool call between your AI agent and MCP servers.
Applying role-based access control principles to AI agent systems for structured permission management.
Indirect prompt injection hides malicious instructions in documents, emails, and tool responses that the agent retrieves, making it harder to detect than direct attacks.
A model extraction attack attempts to steal or replicate an AI model's behavior by systematically querying it and training a copy.
How to benchmark content safety scanners for accuracy, throughput, and latency in AI agent deployments.
Architecture patterns for running AI safety checks at high throughput without becoming a bottleneck.
Approval workflows pause AI agent actions and route them to human reviewers before execution, ensuring humans stay in the loop for high-risk decisions.
Advanced YAML policy patterns for controlling AI agent behavior, including wildcards, nested conditions, and budget limits.
Using geolocation data to restrict AI agent actions based on jurisdictional and regulatory requirements.
Content safety scanning analyzes text flowing through AI agents to detect prompt injection, PII leaks, credential exposure, and other threats.
Data poisoning is an attack that compromises AI system behavior by injecting malicious data into training or retrieval datasets.
Write a YAML policy that controls which tools your AI agent can use, with rules for blocking, allowing, and escalating actions.
Measuring and minimizing the latency that safety checks add to AI agent operations.
Strategies for testing human-in-the-loop approval workflows to verify they trigger correctly and handle all edge cases.
An MCP gateway is a proxy that sits between AI agents and MCP servers, enforcing safety policies on every tool call that passes through.
Go from install to enforcing your first policy in under three minutes with this quickstart.
Implementing time-based restrictions that limit when AI agents can perform specific actions.
An adversarial example is an input specifically crafted to cause an AI system to make incorrect predictions or take wrong actions.
Running AI safety checks at the edge to minimize latency and improve reliability for globally distributed agent deployments.
Testing complete AI agent workflows from user input to final output with all safety checks active.
Production-grade prompt injection defense requires multiple layers: input scanning, policy enforcement, output filtering, and behavioral monitoring.
Installation instructions for the Authensor TypeScript and Python SDKs across all supported package managers.
How to structure policies with inheritance hierarchies and controlled override mechanisms.
Guardrails are runtime constraints that prevent AI agents from taking harmful or unauthorized actions, enforced in code outside the language model.
Distributional shift occurs when the data an AI system encounters in production differs from the data it was trained on.
Using the sidecar proxy pattern to add AI safety checks to existing agent deployments without code changes.
Using snapshot tests to detect unintended changes in policy evaluation behavior.
Install Authensor, write your first policy, and start enforcing safety rules on agent tool calls in under five minutes.
A policy engine evaluates every AI agent action against declarative rules and returns allow, block, or escalate before the action executes.
Practical guidance for writing safety policies that are clear, enforceable, and maintainable.
A step-by-step guide to wrapping AI agent tool calls with policy checks, content scanning, and audit logging.
Goal misgeneralization occurs when a model learns a goal that works during training but fails to transfer correctly to deployment.
How to deploy Authensor and AI safety infrastructure in Kubernetes with production-grade reliability.
Using property-based testing to verify invariants that AI safety policies must always satisfy.
How to safely integrate untrusted or third-party agents into your multi-agent system without compromising security.
Specification gaming is when an AI system satisfies the literal specification of a task while violating its intended spirit.
How attackers use Base64, Unicode, and other encodings to disguise prompt injection payloads and how to defend against them.
Using fuzz testing to discover how AI safety systems handle unexpected, malformed, or adversarial inputs.
Techniques for correlating audit events across multiple agents to reconstruct complete action histories.
Reward hacking occurs when an AI system finds unintended ways to maximize its reward signal without achieving the intended objective.
How crescendo attacks gradually escalate conversations to bypass safety training through incremental boundary pushing.
Preventing safety regressions by testing that existing protections remain effective after changes.
Securing the agent registry and discovery mechanisms that multi-agent systems rely on for coordination.
Deceptive alignment describes a scenario where an AI system appears aligned during evaluation but pursues different goals when deployed.
How to load test AI safety infrastructure to ensure it performs under peak traffic conditions.
Understanding many-shot jailbreaking, where large context windows enable attacks that overwhelm safety training with sheer volume.
Approaches for monitoring AI agent behavior when agents are distributed across multiple services and environments.
AI alignment is the research field focused on ensuring AI systems pursue goals that match human intentions and values.
How few-shot examples in prompts can override safety training and what defenses work against this technique.
Testing that safety guardrails work correctly when integrated with real agent workflows.
Security considerations for the protocols agents use to communicate with each other and with tools.
Retrieval Augmented Generation combines LLM generation with external knowledge retrieval to produce grounded, accurate responses.
Comparing rule-based and machine learning approaches to prompt injection detection with practical guidance on when to use each.
How to write unit tests for AI safety policies that verify individual rules behave as intended.
Organizing multi-agent systems into safety hierarchies where higher levels constrain lower levels.
Function calling enables language models to output structured tool invocations instead of plain text responses.
How trained classifiers detect unsafe content categories in AI agent inputs and outputs.
Designing dashboards that provide actionable visibility into AI agent safety posture.
Design patterns for building supervisor agents that effectively oversee and constrain subordinate agents.
Tool use is the ability of AI agents to interact with external systems by calling structured functions and APIs.
Defining and implementing custom metrics that capture meaningful aspects of AI agent behavior.
Using text embeddings to detect prompt injection attempts by measuring semantic similarity to known attack patterns.
Strategies for preventing a single agent failure from propagating through an entire multi-agent system.
A content filter inspects and blocks or modifies AI agent inputs and outputs that violate defined safety policies.
Recommended tools and architecture for building an observability stack for AI agent systems.
Current jailbreak techniques and the defense strategies that work against them in 2026.
How to detect and prevent agents from being impersonated in multi-agent communication.
A safety classifier is a model or rule system that categorizes content as safe or unsafe based on predefined criteria.
Measuring and reducing the mean time to detect AI agent safety incidents.
How to write system prompts that resist extraction, manipulation, and override attacks.
How attackers exploit shared memory stores to poison agent behavior across an entire multi-agent system.
Red teaming in AI is the practice of systematically probing AI systems for vulnerabilities, failures, and unsafe behaviors.
How attackers exploit context window mechanics to override safety behavior in large language models.
A framework for classifying the severity of AI agent safety incidents to guide response priorities.
Using consensus protocols to improve the safety and reliability of multi-agent decision-making.
Constitutional AI is a training method where models self-critique and revise outputs based on a set of written principles.
Applying chaos engineering principles to test the resilience of AI agent safety infrastructure.
How temperature settings affect safety behavior in language models and what that means for agent deployments.
How to safely delegate privileges between AI agents without creating escalation paths.
Reinforcement Learning from Human Feedback trains models to align with human preferences, but introduces its own safety challenges.
Comparing model-level safety training with runtime safety enforcement and why production systems need both.
Techniques for authenticating messages between AI agents to prevent spoofing and tampering.
The Model Context Protocol (MCP) is an open standard for connecting AI agents to external tools and data sources.
How token-level inspection enables fine-grained safety monitoring for language model outputs.
How to define and enforce trust boundaries when multiple AI agents interact.
Agentic AI refers to systems that autonomously plan, decide, and execute multi-step tasks using tools and external resources.
Techniques for identifying and mitigating hallucinated content in AI agent outputs before they cause harm.
Core security patterns for orchestrating multiple AI agents safely in production environments.
A comprehensive glossary of terms used in AI safety, agent security, and responsible deployment.
A practical guide to filtering LLM outputs before they reach end users or downstream systems.