What is AI detection and response (AIDR)?
AI detection and response (AIDR) is a security capability that monitors your AI systems, prompts, agents, models, and the data pipelines feeding them, then acts when something goes wrong. That matters because AI now touches real customer data, internal systems, and production workflows, so a bad instruction or a steered agent can cause damage in seconds.
AI agents raise the stakes because they can reach into systems with access no user had before, and much of that access goes unchecked. Many of these AI agents and applications ship with no guardrails at all, and the ones that do have them rarely enforce them. That gap is why you need to watch what your AI agents actually do while they run.
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Why AIDR matters now
For years, security tools kept pace with the applications they watched. AI broke that rhythm. Adoption raced ahead of governance while attackers began operating at machine speed, probing and exploiting faster than manual review can follow. AI has also lowered the barrier to entry: tools that generate working code from plain language mean nearly anyone can build functional applications now, which widens the threat surface faster than traditional controls can adapt.
According to the Wiz AI Security Readiness Report, about a quarter of organizations do not know what AI services are running in their environment. This blind spot has a name, shadow AI, meaning AI tools and services used without security review. Agentic AI widens the surface even more, since these software agents plan and take actions on their own, touching systems and data without a human in the loop.
AI threats break the rules older tools were built for. An AI agent interprets intent from a prompt and acts on it, which means it can reach the same goal a different way each time. The agent decides how to act based on what it was asked to do, so what counts as normal keeps shifting.
The stakes climb because agents already hold real access. AI agents need permissions to do their work, to read documents, call APIs, and act on behalf of users, so organizations grant them broad access up front, often without the same scrutiny a human account would face. Because the space is new, teams hand out permissions quickly, sometimes more than the agent's task requires.
Many of these agents run inside sensitive systems with permissions granted ahead of time, so a hijacked agent inherits all of it and can spread fast. Worse, some agents can take action to gain even more access on their own, as happened in real attacks like the Hugging Face incident. The attack often arrives as plain words, not code. A single planted instruction, like a prompt injection or jailbreak, can be the whole exploit.
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Traditional tools (EDR, XDR, and SIEM) in the AI Era
Existing security tools are not obsolete; they remain foundational to modern security operations. However, they were built for a computing model that predates non-deterministic, agentic systems.
Traditional EDR is vital for operating system defense, but enterprise AI may not live on standard host endpoints. Workloads increasingly run in serverless cloud PaaS environments like AWS Bedrock, Azure OpenAI, and Google Cloud Vertex AI, where there is no operating system to install an endpoint sensor on.
AI-targeted attacks also do not look like malware. When an attacker manipulates an agent through prompt injection, the agent does not drop malicious files or trigger kernel hooks. It uses legitimate Non-Human Identity (NHI) credentials to make valid API requests to real databases. To an EDR sensor, this looks like normal business traffic. Even if a tool inspects the prompt text at an endpoint gateway, it still cannot see the cloud control plane behind it, such as the IAM roles the agent holds or the sensitive datastores within its reach.
XDR and SIEM face the same visibility gap. They are built to correlate system logs and network indicators, but an exploited agent operates with authorized credentials across normal ports, appearing entirely legitimate to legacy correlation rules.
This is where AIDR complements the stack. Rather than replacing EDR, XDR, or SIEM, AIDR adds the missing context: analyzing semantic prompt intent, tracking autonomous tool execution, and correlating those actions with cloud infrastructure. By routing these findings back into your existing SIEM and SOAR, AIDR levels up your current investments so the SOC can defend AI alongside the rest of the enterprise.
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What the AI attack surface looks like
The AI attack surface is wider than a single model. It spans the inputs users type, the agents that act, the pipelines that train and serve models, and the tools employees adopt on their own.
| Surface | What goes wrong | What AIDR watches for |
|---|---|---|
| Prompts and inputs | Prompt injection, jailbreaks, and sensitive data pasted into a chat window | Instructions that try to override rules, and data leaving through the input box |
| Agents | Excessive agency, and rogue agent actions when an agent is steered off its assigned task | Actions outside the agent's normal job, and permission use that does not fit the request |
| Models and data pipelines | Data or model poisoning, and model theft | Tampering in training data, and unusual access to model files or weights |
| Shadow AI | Unsanctioned tools, and ungoverned data flows | New AI services appearing without review, and company data moving into them |
Prompt injection deserves a closer look because it is the most common entry point. Direct injection is when an attacker types a malicious instruction straight into the chat. Indirect injection is sneakier, the harmful instruction hides inside a document, web page, or email the AI reads later, so the trap springs without the attacker ever touching your system. The OWASP Top 10 for LLM Applications ranks these risks, listing prompt injection as LLM01 and calling out excessive agency, where an agent holds more power than its task requires, as a separate top threat. MITRE ATLAS adds another view as a framework that catalogs real-world adversary techniques against AI systems, giving teams a shared name for what they see.
The core capabilities of AIDR
None of these capabilities are brand new; they are familiar detection ideas pointed at AI. Here is what you should expect from an AIDR tool, working as a connected loop where each step feeds the next.
Discover and map the full AI attack surface
You cannot protect what you cannot see. AIDR begins with continuous discovery across every layer of the estate. That includes developer workstations, cloud PaaS environments like AWS Bedrock and Azure OpenAI, self-hosted models, and SaaS platforms. This process builds an active AI inventory and AI Bill of Materials (AI-BOM) that catalogs models, datasets, guardrails, and autonomous agents, uncovering unmanaged shadow AI before attackers can exploit it.
Correlate runtime activity with the cloud control plane and identities
A detection on an AI agent in isolation is just noise. True AIDR bridges runtime telemetry with the cloud control plane. By correlating the agent or model with its underlying Non-Human Identity (NHI), IAM permissions, network reachability, and accessible datastores, the system maps the true blast radius. This correlation is what tells an analyst whether a prompt injection hit an isolated playground or an agent equipped with administrative credentials and direct access to customer databases.
Baseline agent behavior and reconstruct causal execution chains
Autonomous agents do not merely answer questions; they invoke tools, query external APIs, and execute actions via protocols like Model Context Protocol (MCP). AIDR establishes baselines for normal agent behavior and reconstructs the full causal chain: from the initial prompt, through the model's reasoning, to downstream tool calls and cloud infrastructure access. When an agent deviates by querying an unapproved datastore or making abnormal API calls, the system catches the anomaly before unauthorized actions can spread.
Inspect prompts and model responses semantically
AIDR inspects the actual conversation payload: the prompts going in and the responses coming out. Unlike traditional network inspection that only evaluates packet headers, AIDR analyzes semantic intent. It flags direct and indirect prompt injections, jailbreak attempts, and system prompt leakage, while preventing confidential data like PII, secrets, and corporate IP from escaping in a model's reply. Mature platforms map these detections directly to frameworks like MITRE ATLAS so AI threats speak the same language as the rest of the SOC.
Automate investigation and triage at machine speed
Prompt-level alerts can quickly overwhelm a security team. To prevent alert fatigue, modern AIDR incorporates automated, agentic investigation into the triage loop. The system gathers contextual evidence on its own, reviews the execution trail, and assesses the threat severity like a seasoned incident responder. By delivering a clear verdict and root-cause analysis up front, it ensures analysts spend their time acting on verified incidents rather than triaging raw prompts.
Contain threats in real time across code, cloud, and runtime
When a threat is confirmed, AIDR must intervene with surgical, context-aware containment actions before harm can spread:
Block malicious prompts: Stop hostile payloads before the model processes them.
Redact sensitive responses: Strip secrets or personal data before delivery to the user or downstream workflow.
Quarantine rogue agents: Pause an agent's execution loop if it has been manipulated by prompt injection.
Revoke credentials and permissions: Instantly invalidate the compromised session tokens or cloud IAM credentials the agent was granted.
Because these capabilities link code, cloud posture, and runtime activity into one system, detections flow naturally into existing SIEM and SOAR pipelines, giving the SOC end-to-end visibility from a vulnerable prompt to the affected cloud asset.
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AIDR vs EDR, XDR, and NDR
These are detection and response layers, not competitors. Each watches a different part of your environment, and AIDR covers the AI layer the others cannot see.
| Capability | EDR | XDR | NDR | AIDR |
|---|---|---|---|---|
| Primary telemetry | Endpoint processes and files | Correlated signals across sources | Network traffic | Prompts, agent actions, and model activity |
| Core question | What is running on this device? | How do these signals connect? | What is moving across the network? | What is the AI being asked to do, and why? |
| Sees prompt injection | No | No | No | Yes |
| Sees agent over-reach | No | Partial | No | Yes |
| Sees shadow AI | No | No | Partial | Yes |
| Maturity | Established | Established | Established | Emerging |
What to look for in an AIDR solution
Full-stack architectural coverage: attackers target the easiest path in, whether that is a managed PaaS service like Amazon Bedrock or Azure OpenAI, a self-hosted open-source model, an autonomous agent, or a third-party SaaS tool. The solution must provide visibility across your entire AI footprint rather than securing one hosting model in a silo.
Detections mapped to AI threat frameworks: generic anomaly rules create noise. Look for detection logic mapped directly to established industry standards like MITRE ATLAS and the OWASP Top 10 for LLMs. This ensures your team can categorize, track, and report on AI-specific tactics such as indirect prompt injection, model evasion, and training data poisoning.
Build-to-runtime correlation: when a tool connects how an agent was built to what it does live, you can tell a design flaw from an active attack.
Proportional response: the tool should match the action to the threat, redacting one reply or quarantining a whole agent, so a small issue does not trigger a big outage.
Cloud, identity, and data context: an AI alert means little on its own, so the tool should link it to the identity, data, and exposure around it to show real impact.
Fits your existing SOC: findings should route into the SIEM and SOAR your team already uses, so AI threats join one workflow instead of a new silo.
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How Wiz secures the AI attack surface
Wiz starts from context. It connects code, cloud, and runtime into one context graph, a live map of every resource, identity, permission, and data store across your environment. Because of that map, a detection never arrives alone. It shows which identity acted, what data and systems the AI could reach, and how far a real attack has and could spread.
Wiz Defend ingests and correlates invocation logs on the security graph, the record of what goes into a model and what comes back. Those logs help it catch attacks like prompt injection and flag sensitive data leaking out in a model's reply. Each detection also carries an AI context tag.That tag tells an analyst whether a rule fired on an agent, a workload, or an identity, which shifts triage and containment.
When an alert fires, the Blue Agent investigates on its own and gives analysts a comprehensive investigation and verdict, gathering context and assessing the nature of the threat like a trained incident responder would. Blue Agent investigations not only help teams save time by helping them prioritize threats, but it empowers the SOC with the context they need to investigate and remediate efficiently and effectively. The rule set keeps growing with AI-specific detections mapped to MITRE ATLAS, a public catalog of AI attack techniques. For teams already running Wiz Defend, this extends the detection layer they use today instead of adding a new product.
When an alert fires, the Blue Agent investigates on its own and gives analysts a comprehensive investigation and verdict, gathering context and assessing the nature of the threat like a trained incident responder would. Blue Agent investigations not only help teams save time by helping them prioritize threats, but it empowers the SOC with the context they need to investigate and remediate efficiently and effectively. The rule set keeps growing with AI-specific detections mapped to MITRE ATLAS, a public catalog of AI attack techniques. For teams already running Wiz Defend, this extends the detection layer they use today instead of adding a new product.
Prevention works the other side of the loop. AI security posture management (AI-SPM) reduces risk before runtime by automatically discovering unmanaged and shadow AI across your cloud and code. It builds a centralized AI inventory and a continuously updated AI Bill of Materials (AI-BOM / AI-SBOM), cataloging models, agents, MCP servers, libraries, and datasets across development and production. From that inventory, AI-SPM surfaces exposed models, weak guardrails, and sensitive data sitting where it should not, stopping risks before they ever reach production. Because detection and posture share the same context graph, a runtime alert and an underlying posture gap point back to the same root cause instead of two disconnected tickets.
The payoff shows up in real environments. Each Wiz detection arrives with cloud context already attached and fits the workflow your SOC already runs. Your team spends less time gathering evidence and more time acting on it. Request a demo to see how this plays out across your own cloud and AI environment.
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