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