New paper defines the five pillars of Agent Integrity — Intent Alignment, Identity and Attribution, Behavioral Consistency, Agent Audit Trails, and Operational Transparency — and details the technical capabilities required to achieve them.
What’s inside: An in-depth technical resources about all forms of Shadow AI and how to manage them.
What’s inside: Understanding how Model Context Protocol creates Shadow AI deployments
What’s inside: Deep explainer about semantic privilege escalation and what it means for agent security.
What’s inside: A deeper look at the compliance and governance challenges of Generative AI.
What’s inside: An in-depth whitepaper about the unique AI security needs of credit unions.
What’s inside: CIO Upside covers Acuvity’s State of AI Security report, discussing shifts in ownership.
AI security is the discipline of protecting artificial-intelligence systems — including models, agents, connectors, tools, and runtime execution flows — from misuse, malfunction, unauthorized access, or adversarial invocation. It extends beyond traditional cyber controls by addressing novel vectors such as prompt-injection, tool-call chains in agentic systems, unsanctioned model usage, and context manipulation during live AI operations.
Key elements of AI security include:
Agent and tool-chain security: In systems where agents use external tools or services (for example invoking a document retrieval system, writing to databases, or orchestrating other agents) the security surface expands to include the entire call graph — permissions on tool invocation, identity of the agent, policy on tool results, chaining control and audit.
Runtime inspection and enforcement: AI security requires visibility into inputs, context, model invocation, agent workflows, tool interactions, and outputs — all in real time — and enforcement of policy at those points (blocking, sanitizing, approval gates). Without runtime controls, risks persist even if design-time controls exist.
Lifecycle management: From model training and fine-tuning, through deployment, to retirement, AI security demands controls on data provenance, model integrity, versioning, drift monitoring, adversarial testing, access control, and audit logging.
Governance, trust and compliance: As defined in Gartner’s AI TRiSM (Artificial Intelligence Trust, Risk and Security Management) framework, AI security aligns with governance (visibility, accountability), risk and security management (model robustness, data protection, adversarial resistance), and runtime inspection & enforcement as one of the core layers.
Emerging threats specific to AI: These include agentic systems acting autonomously, tools being misused through model-calls, prompt and context attacks (e.g., injection, jailbreak), hidden agent workflows performing unmonitored actions, and unsanctioned “Shadow AI” systems operating outside enterprise controls.
Policy as Code is a security approach that treats agent security policies as version-controlled, machine-readable configuration files (manifests) rather than asking developers to implement security in application code.
Instead of requiring each development team to build security into their agents differently, a manifest defines what security looks like for each agent through a YAML file. This file describes which services the agent can communicate with, what security controls apply, how user identity must be maintained, and what behaviors are expected vs. anomalous.
The key advantage: developers can build agents using any framework (CrewAI, LangChain, LangGraph), deploy to any environment (Kubernetes, serverless, VMs), and use any LLM—as long as the deployed agent conforms to its manifest. Security teams maintain centralized control without requiring code changes, and the same manifest works across heterogeneous environments automatically.
Think of it like infrastructure-as-code for security: declarative, auditable, and enforced at the platform level rather than relying on developer implementation.
Accordion Content
Accordion Content
The Acuvity demo explains how AI security, AI governance, and runtime inspection and enforcement combine to keep AI use accountable and within policy.
It shows how the platform observes model and agent operations in runtime, applies enforcement when activity falls outside approved parameters, and sustains continuous assurance by validating that governance and security controls are consistently applied. The session also covers how agent security enforces limits and oversight for connected systems.
Use runtime inspection and enforcement to monitor and secure AI activity, agents and MCP servers.
Operationalize AI governance through enforceable policies.
Maintain continuous assurance by confirming controls remain active and effective.
Apply agent security to define and monitor boundaries for automated systems.
Build a cohesive AI security approach that scales with adoption.
