AI Agent Security: Top 8 Tools & Threats for 2026

updated on May 1, 2026

AI agent security means two different things in practice: locking down the autonomous agents your organization has deployed, and using AI agents to run security operations faster than human analysts can.

We analyzed eight platforms across both categories, the attack vectors making them necessary, and the compliance deadlines are now forcing the conversation.

AI agent security tools and platforms

The market is divided into two categories.

Category 1: Securing AI agents (AI-SPM & runtime protection): Focuses on securing the AI agents you have deployed: scanning models before deployment, controlling what agents can access, and blocking attacks like prompt injection at runtime.
Category 2: AI agents performing security operations (Agentic SOC): Deploys AI agents to perform security operations autonomously, replacing or augmenting human analysts in SOC workflows.

*Azure ML registries only, third-party or self-hosted models not covered.

Agent Identity Governance: OAuth token monitoring, non-human identity (NHI) governance, and least-privilege enforcement for deployed agents.
MCP Security: native monitoring of Model Context Protocol server connections.
Agentic Red Teaming: multi-turn, multi-agent attack simulation distinct from standard LLM red teaming.

1. AI Security Posture Management (AI-SPM) tools

Palo Alto Networks Prisma AIRS

Prisma AIRS (AI Runtime Security) is the only platform in this comparison claiming end-to-end coverage of the agentic AI lifecycle from pre-deployment model scanning through live runtime enforcement as a product distinct from Prisma Cloud, the company’s CNAPP offering.¹

The AI Agent Gateway (a central control plane enforcing identity-based access controls for autonomous agents during live execution), Agent Artifact Security (architecture scanning before deployment), and built-in AI Red Teaming for multi-agent systems.² The platform absorbed Protect AI’s ModelScan serialization vulnerability detection following Palo Alto Networks’ July 2025 acquisition. Koi’s agentic endpoint security is pending full integration and remains a separate deployment as of Q2 2026.³

Pros:

Pre-deployment AI Model Scanning detects model tampering, malicious scripts, and deserialization attacks.
Built-in AI Red Teaming supports multi-turn attack simulation and multi-agent system testing.
Runtime safeguards block prompt manipulation and data exposure during live agent interactions.⁴

Cons:

Pricing is not publicly listed; enterprise contracts are required.
Koi endpoint integration is ongoing; locally running agents outside cloud and SaaS perimeters require a separate deployment until that completes.

Wiz AI-SPM

Wiz AI-SPM’s core differentiator is the Wiz Security Graph, which correlates AI risks with the underlying cloud infrastructure context.⁵ Where most AI-SPM tools report AI risks in isolation, the Security Graph shows that a vulnerable model is running in a production VPC with an internet egress context that changes remediation priority.

The platform includes Mika AI for natural language risk queries (“which LLMs are accessing production databases?”), Wiz Blue Agent for automated threat investigation, and Wiz Defend for runtime protection against prompt injection and rogue agent behavior.⁶ Shadow AI discovery operates agentlessly, scanning Hugging Face model repositories for malicious code and training data for PII exposure.

Pros:

Generates AI Bill of Materials (AI-BOM) covering models, frameworks, and dependencies powering each AI system.⁷
Discovers shadow AI workloads and unmanaged AI services without agent deployment.⁸
Code-to-cloud correlation links application-level AI risks to the infrastructure configuration, enabling them.

Cons:

Native SaaS Security Posture Management (SSPM) is not included; SaaS coverage requires a supplementary tool.
Pending Salesforce acquisition introduces roadmap uncertainty for non-Salesforce environments.

Obsidian Security

Enterprise AI agents operate inside Salesforce, Microsoft 365, Google Workspace, and dozens of other SaaS platforms, accumulating OAuth tokens and moving data at volumes no human user would. Obsidian Security is built specifically for this environment.

According to Obsidian’s own research data, 90% of enterprise agents are over-permissioned, agents move 16 times more data than human users, and 53% of AI agents access sensitive information.⁹ The platform detects over-permissioned agents, identifies token compromise, tracks app-to-app data movement, and surfaces shadow AI deployments connected to corporate SaaS accounts without IT visibility.

Pros:

SaaS Security Posture Management (SSPM) provides continuously updated views of misconfigurations and compliance gaps across SaaS applications.
Detects AI prompt injection and data leakage within GenAI SaaS applications, including Copilot and Agentforce.[
Non-human identity governance covers connected apps, OAuth tokens, and service accounts with behavioral baselines.¹⁰

Cons:

Coverage is SaaS-focused; organizations running AI agents in self-hosted or cloud-native infrastructure need a separate tool for infrastructure-layer visibility.
Pricing is not publicly listed.

Microsoft Defender for Cloud

Microsoft Defender for Cloud’s AI security module extends the existing CSPM platform with AI-specific threat detection no additional deployment required for organizations already running Defender for Cloud.¹¹ Teams managing Azure AI Foundry, Copilot Studio agents, or Azure OpenAI deployments get threat detection and posture management within the same console.

Pricing is one of the few publicly listed figures in this category: $0.0008 per 1,000 tokens scanned per month, with Foundry agents included at no additional charge and a 30-day trial capped at 75 billion tokens.¹²

The module integrates with Azure AI Content Safety Prompt Shields for jailbreak detection and routes alerts for data leakage, credential theft, and anomalous agent behavior into Microsoft Sentinel. ¹³ In March 2026, Microsoft launched Agent 365 a unified control plane for governing and securing agents across the Microsoft environment at $15 per user per month.¹⁴

Pros:

No additional infrastructure required for Azure-native organizations already using Defender for Cloud CSPM.
Transparent token-based pricing allows cost estimation before deployment.
Prompt Shields integration handles jailbreak and prompt injection detection natively.¹⁵

Cons:

AI model security scans only within Azure Machine Learning registries and workspaces, not third-party or self-hosted model registries.¹⁶
Limited value for organizations running AI agents primarily outside Azure

HiddenLayer MLDR

AI security tools focus on agent behavior, data access, and prompt injection. HiddenLayer Machine Learning Detection and Response (MLDR) operates one layer deeper: the model.

MLDR monitors models in production for adversarial attack evasion attacks that manipulate inputs to cause misclassification, model inversion attacks that reconstruct training data from model outputs, and membership inference attacks that determine whether specific data appeared in training.¹⁷ It runs agentlessly, requiring neither access to training data nor model weights, making it compatible with proprietary or third-party models.

Supply chain scanning covers model repositories before deployment, detecting backdoors embedded in serialized model files the attack class MITRE ATLAS catalogs as AML.T0010 (ML Supply Chain Compromise).¹⁸ The AI Attack Simulation module continuously red-teams deployed models as versions and environments change.¹⁹

Pros:

Detects evasion, model inversion, and membership inference attacks in real time without requiring training data access.
Pre-deployment supply chain scanning identifies serialization vulnerabilities and backdoors in model files.
AI Guardrails enforce runtime policy compliance, including prompt injection and data leakage prevention.²⁰

Cons:

Does not provide SaaS Security Posture Management or non-human identity governance.
Pricing is not publicly listed.

Lakera Guard

Lakera Guard sits between your GenAI application and the LLM, intercepting every prompt before it reaches the model and every response before it returns to the user.²¹ Its threat intelligence database draws from over 80 million prompts collected through the Gandalf public red teaming challenge, giving the detection engine exposure to attack patterns that enterprise-internal red teams rarely encounter.²²

The platform covers OWASP LLM Top 10 risks and operates at sub-millisecond latency via an API-first design, which matters for customer-facing applications where detection latency adds directly to user response time.²³ A free tier is available at platform.lakera.ai, making initial integration and testing accessible without a sales process.

Context from April 2026 research: Google and Forcepoint separately confirmed that indirect prompt injection payloads are now actively embedded in public web content at scale, waiting for AI agents to process them.²⁴ Lakera’s indirect injection detection covers this attack class, including payloads retrieved from external URLs that the agent browses during task execution.

Pros:

Free tier available for development and initial testing without vendor engagement.
Indirect prompt injection detection covers document-embedded and web-retrieved malicious instructions.
AI Red Teaming with risk-based vulnerability management and direct/indirect attack simulations.²⁵

Cons:

Does not perform pre-deployment AI model file scanning or backdoor detection.
Runtime focus means it cannot assess infrastructure or SaaS posture risks.

2. Agentic SOC Platforms

CrowdStrike Falcon

Charlotte AI operates as an autonomous investigation and response layer within the CrowdStrike Falcon platform, combining EDR, XDR, SIEM, and SOAR under a conversational natural language interface.²⁶ When an alert fires, Charlotte AI gathers evidence, correlates telemetry across endpoints and identities, and presents a verdict with reasoning then waits for analyst approval before executing containment actions.

That approval gate is a deliberate architectural choice. CrowdStrike’s Agentic SOAR combines scripted automation with AI reasoning, with configurable autonomy levels: supervised execution for high-impact actions, autonomous execution for low-risk containment.²⁷ The platform uses reinforcement learning from analyst feedback, improving decision accuracy as it accumulates institutional knowledge specific to each customer’s environment.

CrowdStrike holds ISO 42001 AI Governance certification the only product in this comparison with independent third-party validation of its AI governance controls.²⁸ At RSAC 2026, the company announced Shadow AI Discovery extending across endpoints, SaaS, and cloud environments, identifying more than 1,800 AI applications running on enterprise devices across its customer base.²⁹

Pros:

ISO 42001 AI Governance certification provides third-party validation of AI management controls.³⁰
Natural language threat hunting converts analyst questions into structured queries across the entire Falcon data lake.
Configurable autonomy levels allow organizations to control which actions require human approval.³¹

Cons:

Charlotte AI does not function as a guardrail or firewall for third-party GenAI applications; its focus is infrastructure threat detection, not AI model security.
Pricing starts at $8.99 per endpoint per month, which scales significantly for large device fleets.³²

SentinelOne Purple AI

Purple AI is embedded inside Singularity XDR rather than sold as a standalone product, which means analysts interact with it in the same interface they use for every other investigation.³³ An analyst asks “What is the root cause of this alert?” in natural language; Purple AI queries the underlying data, correlates telemetry across endpoints, cloud, and identity systems, and returns an answer with source evidence attached.

This integration model contrasts with AI investigation tools that require exporting data to a separate system. Purple AI has access to the full Singularity data lake and uses self-learning from prior incident data to improve correlation accuracy over time.³⁴ The platform includes a native AI-SIEM backend for large-scale ingestion, and Singularity Hyperautomation handles workflow automation across the response chain.³⁵³⁶

Pros:

Natural language threat hunting translates plain English questions into optimized queries across the enterprise data lake.
Cross-domain correlation spans endpoints, cloud assets, and identity in a single investigation workflow.
Native AI-SIEM backend avoids per-ingest pricing penalties at scale.³⁷

Cons:

Does not inspect or block prompts in customer-deployed GenAI applications; focus is infrastructure and endpoint telemetry.
Custom enterprise pricing only; no public rate card available.

Common features across AI agent security platforms

Despite covering different layers of the stack, all eight platforms in this comparison provide five baseline capabilities that organizations can expect from any enterprise vendor in this category.

AI and ML asset discovery maps deployed models, agents, connected tools, and data sources the prerequisite for any security program. Without a current inventory, teams cannot assess exposure or enforce policy.
Behavioral anomaly detection monitors AI workloads for deviations from established baselines. The mechanism varies (unsupervised ML at Darktrace, statistical thresholds elsewhere), but the output is the same: alerts when an agent does something unexpected querying databases it has not accessed before, making unusual API calls, or processing volumes of data outside normal ranges.
Integration with SIEM and SOAR platforms allows findings to feed into existing security workflows via documented APIs. Organizations should verify whether vendors support bidirectional integration (sending alerts to SIEM and receiving enrichment back) versus read-only log forwarding.
Audit logging for compliance captures security events, agent actions, and access decisions in queryable formats. EU AI Act Article 9 requirements for high-risk AI systems and SOC 2 Trust Services Criteria both mandate this capability by August 2026 for organizations in scope.³⁸
Alerting and notification workflows surface detected threats through dashboards, email, Slack, or ticketing system integration so that security teams receive actionable information rather than raw log output.

AI agent security threats

AI agents introduce attack surfaces that traditional security controls were not built to address. A signature-based EDR detects malware. It cannot detect an agent that received legitimate instructions from a malicious document it was asked to summarize, then used its own legitimate credentials to exfiltrate data. The following threats define why specialized tooling is required.

1. Prompt injection and jailbreaking

Prompt injection embeds adversarial instructions inside content that agents process web pages, emails, PDF attachments, and tool outputs, causing the agent to execute commands the operator never authorized. OWASP ranks prompt injection as the top vulnerability in its LLM Top 10, present in over 73% of production AI deployments assessed during security audits.³⁹

The threat is no longer theoretical. In April 2026, Google and Forcepoint independently published evidence of indirect prompt injection payloads embedded at scale in public web content static websites, blogs, and comment sections waiting for AI agents to retrieve and process them.⁴⁰ Ten confirmed live payloads targeting AI agents with objectives including financial fraud, data destruction, and API key theft were catalogued in the same research period.⁴¹

Unit 42 found that 85.2% of real-world injection attempts use social engineering techniques rather than simple command overrides, including concealment methods such as zero-width Unicode characters and base64-encoded payloads.⁴² A meta-analysis of 78 studies published in January 2026 found that adaptive attack success rates against state-of-the-art defenses exceed 85%.⁴³

In 2025, Microsoft 365 Copilot was found vulnerable to CVE-2025-32711 (EchoLeak), a zero-click prompt injection exploit allowing remote data exfiltration through crafted emails without user interaction.⁴⁴ The Model Context Protocol (MCP) enables a related attack class: tool poisoning, where malicious instructions embedded in tool metadata instruct agents to forward sensitive data to attacker-controlled endpoints.⁴⁵

2. Token compromise and credential theft

AI agents authenticate to external services using OAuth tokens and API keys. Those credentials grant the agent the same permissions as the identity they represent and an attacker who obtains them inherits those permissions without triggering MFA, because the token itself is the authentication artifact.

The August 2025 Salesloft/Drift breach demonstrates the cascading risk. Attackers compromised a third-party chat agent integration and extracted OAuth refresh tokens, then used those tokens to impersonate the Drift application against over 700 downstream customer environments for ten days, accessing Salesforce data, Google Workspace accounts, and cloud credentials without triggering authentication alerts.⁴⁶ Detection failed because the OAuth queries originated from a pre-approved application identity identical to legitimate traffic in standard authentication logs.⁴⁷

Research published in early 2026 from UC Santa Barbara and Fuzzland reinforces this at the infrastructure level. Researchers purchased 28 paid LLM API routers and collected 400 free ones; 9 were actively injecting malicious code into model responses. A honeypot configured with a leaked OpenAI key resulted in 100 million GPT tokens burned and 401 sessions hijacked 401 of which were already running in “YOLO mode” with no human approval gates.⁴⁸ No LLM API provider enforces cryptographic integrity on the response path, meaning a malicious router can inject instructions into model responses and the agent will execute them as legitimate tool calls.

3. Over-permissioning and privilege escalation

Ninety percent of enterprise agents are over-permissioned.⁴⁹ This happens for structural reasons: SaaS platforms default to broad OAuth scopes during agent authorization, agents inherit permissions from departed employees whose accounts were never cleaned up, and permissions accumulate without review as agents are extended to handle new tasks.

OWASP places excessive agency in the top three agentic AI security risks for 2026, defining it as agents having capabilities beyond what tasks require.⁵⁰ The attack consequence is semantic privilege escalation: an agent instructed to perform a legitimate task autonomously acquires access outside intended scope through chains of autonomous decisions, with no single step triggering a security alert.

Okta analysis confirms that AI agents must be treated as privileged users subject to the same access controls, rotation policies, and audit trails applied to human administrators noting most organizations currently lack those controls for non-human identities.⁵¹ Enterprises now manage an average 82:1 machine-to-human identity ratio, with agent proliferation outpacing governance infrastructure.⁵²

4. Shadow AI and unauthorized agent deployments

When a business unit connects Salesforce Agentforce to the company’s Google Workspace without IT involvement, the result is an agent with access to production SaaS data, no security review, no access logging, and no way to revoke credentials quickly if the agent behaves unexpectedly. Eighty-seven percent of organizations have Microsoft Copilot enabled, making shadow agent discovery a nearly universal need rather than an edge case.⁵³

Shadow AI breaches cost an average of $670,000 more than standard breaches ($4.63 million versus $3.96 million), driven by the delayed detection that comes from agents operating outside monitored environments.⁵⁴ Sixty-three percent of employees using AI tools paste sensitive company data into personal chatbot accounts, creating data residency violations that compliance teams are often unaware of until a breach surfaces them.⁵⁵

IDC projects that 60% of AI failures in 2026 will result from governance gaps rather than model performance issues a framing that shifts accountability from AI vendors to the organizations that deploy agents without adequate controls.⁵⁶

5. AI supply chain attacks and model tampering

Enterprises sourcing models from Hugging Face, using PyPI packages for ML frameworks, or connecting agents to third-party MCP servers inherit whatever vulnerabilities exist upstream. MITRE ATLAS catalogs these techniques under AML.T0010 (ML Supply Chain Compromise), documenting backdoors in serialized model files, poisoned training data injected into public datasets, and malicious dependencies in ML framework packages.

In February 2026, the ClawHavoc campaign systematically poisoned OpenClaw’s skill marketplace the first AI agent registry attacked at scale. Over 1,100 malicious skills were uploaded masquerading as productivity and developer tools, with several becoming among the most-downloaded packages on the platform before detection. IBM X-Force confirmed over 21,000 exposed instances.⁵⁷ A concurrent audit found that 43% of publicly available MCP servers contain command execution vulnerabilities, and 36.7% are exposed to server-side request forgery attacks.⁵⁸

Memory poisoning attacks inject malicious instructions into agent memory stores, creating persistent compromises that activate days or weeks after the initial infection. Research demonstrated injection success rates above 95% against production-style agents through query-only interaction.⁵⁹ In multi-agent systems, a single compromised agent poisoned 87% of downstream decision-making within four hours.⁶⁰

Compliance and governance frameworks for AI agent security

Enterprise AI agent deployments face overlapping compliance requirements from international standards, sector-specific regulations, and regional legislation that are now moving from voluntary guidance to enforceable mandates.

ISO/IEC 42001, published December 2023, specifies requirements for Artificial Intelligence Management Systems (AIMS) with 38 distinct controls covering risk assessment, transparency, and continuous improvement.⁶¹ Certification is voluntary but satisfies several EU AI Act quality management requirements.⁶² Among the platforms in this comparison, CrowdStrike Charlotte AI holds ISO 42001 certification the only product with this specific independent audit validation.⁶³
NIST AI Risk Management Framework (AI RMF 1.0) organizes AI governance into GOVERN, MAP, MEASURE, and MANAGE functions.⁶⁴ In February 2026, NIST’s Center for AI Standards and Innovation (CAISI) formally launched the AI Agent Standards Initiative the first US government program dedicated to developing voluntary standards specifically for agentic AI security.⁶⁵ An AI Agent Interoperability Profile with specific controls for agent identity authentication, least privilege, and prompt injection prevention is scheduled for Q4 2026.⁶⁶ On April 7, 2026, NIST additionally released a concept note for an AI RMF Profile on Trustworthy AI in Critical Infrastructure, signaling that agentic AI in regulated sectors is a priority.⁶⁷
EU AI Act enforcement of high-risk AI system requirements begins August 2, 2026.⁶⁸ Organizations running AI agents in finance, HR, healthcare, or critical infrastructure must meet conformity assessment requirements, with penalties reaching EUR 35 million or 7% of global annual turnover for violations.⁶⁹ The Act requires high-risk AI systems to maintain technical documentation, allow external monitoring, implement structured human oversight with intervention points, and include revocation mechanisms that can halt agent operation quickly.⁷⁰ Article 50 transparency requirements including machine-readable marking for AI-generated content also take effect August 2026. The EU AI Office has not yet issued detailed guidance specific to agentic systems, creating compliance uncertainty that organizations must resolve through their own interpretation of the Act’s general principles.⁷¹
OWASP Top 10 for Agentic Applications 2026, released December 2025, provides the first globally peer-reviewed security risk taxonomy for autonomous AI systems, developed with input from over 100 security researchers.⁷² The ten risk categories (ASI01 through ASI10) cover Agent Goal Hijacking, Tool Misuse and Exploitation, Delegated Trust Abuse, and Agentic Supply Chain Vulnerabilities, among others. AWS, Microsoft, NVIDIA, and GoDaddy have each referenced or implemented guidance from the framework in production. Organizations subject to the EU AI Act will increasingly find OWASP Agentic Top 10 coverage required in vendor security assessments.
SOC 2 applies AI agents as subservice organizations under Trust Services Criteria, requiring scoped API keys, transaction limits, and comprehensive audit trails of agent decisions.⁷³ GDPR Article 22 grants individuals rights regarding solely automated decisions with legal effects, requiring meaningful human involvement and contestability mechanisms for agents making consequential decisions.⁷⁴

FAQ

EDRs detect malware and endpoint anomalies. They don’t inspect what an AI agent does with a compromised OAuth token, whether a model was backdoored before deployment, or whether a prompt embedded in a document redirected the agent’s behavior. The tooling gap is real.

Depends on what you’re doing with AI. If you’re deploying agents that connect to SaaS platforms, process external content, or use third-party models, then yes, Charlotte AI and Purple AI secure the SOC; they don’t govern the agents themselves. If you’re only using AI internally within those platforms, the existing coverage may be sufficient.

As of April 2026, it’s a production threat. Google and Forcepoint separately published evidence of indirect prompt injection payloads embedded in public web content at scale static sites, and blogs seeded with instructions targeting AI agents. Ten confirmed live payloads were catalogued in the same reporting period.