How Agentic Security Addresses the Risks Traditional Security Misses
Traditional security tools were built to protect users, endpoints, & networks. Learn where it falls short for AI risks and the best way to secure your agents.
October is Cybersecurity Awareness Month, making it a perfect time to take a closer look at how the security landscape is changing as AI agents become part of the enterprise workforce.
Most organizations already have layers of security protecting their users, endpoints, identities, networks, applications, and data. While these controls remain essential, AI agents introduce novel risks that traditional tools were simply not designed to detect or prevent.
Because an AI agent can reason over context, query enterprise data, and execute actions across interconnected systems—often driven by model overeagerness—a compromise rarely looks like a typical security breach. In an effort to be helpful, an agent may aggressively complete ambiguous tasks without verifying safety boundaries, executing unintended or high-risk actions even while using completely valid credentials.
What is the difference between traditional security and agentic security?
To understand why this gap exists, it helps to examine the functional core of traditional security alongside the emerging requirements of agentic security.
Traditional security architecture centers on protecting infrastructure, user identities, networks, and data repositories. Teams implement IAM and PAM for access control, EDR and CNAPP for monitoring, DLP and API gateways for data movement, and AI guardrails for basic input/output filtering.
These solutions effectively address foundational questions such as:
- Who or what is accessing the system?
- Is this identity authorized?
- Is this endpoint behaving suspiciously?
- Is this request allowed?
- Is sensitive data leaving the environment?
- Does this input or output violate a known policy?
Traditional security controls aren’t prepared for the new question that AI agents introduce:
Should the agent be taking this action, in this context, as part of this task?
Consider an AI coding agent with authorization to access code repositories, execute commands, and interface with development tools. Traditional controls will successfully validate the user's identity, confirm the agent's permissions, and approve individual API calls.
Yet if malicious instructions enter through an untrusted code comment, documentation file, or third-party tool response, the agent can be manipulated into executing harmful actions. Because each individual step appears legitimate in isolation, the risk only becomes visible when evaluating the full sequence of events in context.
That is where agentic security is vital for teams to have.
Why traditional security can miss agentic attacks
Here’s another example: there’s an AI assistant with access to an employee's email, documents, cloud storage, and internal applications. An attacker sends an email containing instructions designed to manipulate the agent. The email looks ordinary to the employee. The agent reads it as part of its normal workflow and follows the instructions.
Subsequentially, the agent retrieves sensitive credentials from connected storage and transmits them to an external endpoint accessible by the attacker.
From the perspective of legacy security tools, every operation looks clean:
- The employee is authorized to access the mailbox.
- The agent is authorized to read the email.
- The agent is authorized to access the connected files.
- The API request is authenticated.
- The data transfer may use a legitimate application or connection.
The true hazard lies in the broader context and sequence of those actions.
That is a fundamental characteristic of agentic threats. As you can see in Straiker's one-pager at the bottom of this article, while isolated steps appear authorized, evaluating the entire workflow against the agent's intended goal reveals the compromise.
This shift extends to the attack vector itself. Rather than relying on traditional payloads like malware, credential theft, or software exploits, agentic attacks leverage untrusted content embedded in emails, web pages, code comments, or tool outputs to hijack decision-making.
Do AI agents need their own security stack?
Yes. If an AI agent can access business systems, sensitive data, or tools that can take action, security teams need clear visibility into what that agent can do and targeted controls designed around how it operates.
The biggest initial hurdle is simple discovery. Enterprise agents manifest in many places such as custom internal builds, third-party platforms, developer tools, and unsanctioned shadow AI. You can’t protect agents you don’t know exist.
From there, teams need to understand the agent's permissions and connections, test whether it can be manipulated, and protect it while it is operating.
A complete agentic security strategy requires three core capabilities:
- Discover active agents, connected tools, permissions, and MCP servers across your environment.
- Test agent responses against realistic adversarial techniques and prompt injection attacks.
- Defend active agents at runtime by intercepting and blocking unsafe actions, backed by an agentic kill switch to instantly halt compromised behavior.
Straiker’s agentic security platform delivers on this framework: Discover AI handles visibility and posture management, Ascend AI provides automated red teaming, and Defend AI delivers continuous runtime protection and the industry’s first Agentic Kill Switch to instantly halt compromised behavior.
The best agentic security system for your AI agents
Discover AI helps you see every agent and what it can reach
What it does: Continuously maps your AI ecosystem, including MCP servers, Claude Skills, tools, and integrations across platforms like Amazon Bedrock AgentCore, Azure AI Foundry, Microsoft Copilot Studio, and coding assistants like Cursor, Claude Code, and GitHub Copilot. Using AI-SPM and Agent-SPM, it evaluates permissions, blast radius, and configurations to flag over-permissioned agents, risky MCP links, and misconfigurations. Findings map directly to the OWASP Top 10 for Agentic Applications, MITRE ATLAS, and NIST AI RMF, giving compliance teams audit-ready evidence for frameworks like the EU AI Act.
In practice: When a developer connects a coding agent to a community MCP server for database queries—exposing shell execution and live cloud credentials—Discover AI immediately flags the high-risk combination before it can be exploited.
Where traditional security falls short: EDR sees an approved developer tool running normally and network monitoring sees standard encrypted traffic, completely missing that the agent can execute shell commands with production credentials. For more details, see our post on why firewalls and EDR still miss shadow AI.
Ascend AI helps you find attack paths before attackers do
What it does. Ascend AI autonomously red teams your agents using AI attack agents that replicate real-world adversarial behavior. It tests single-turn and multi-turn interactions for prompt injection, tool misuse, data exfiltration, and logic exploits. Embedded into CI/CD pipelines, every model update, prompt tweak, or config change triggers an automated assessment tied to OWASP, MITRE ATLAS, NIST, and EU AI Act controls. Each finding comes with remediation guidance and helps tune the guardrails that protect the agent in production.
In practice. Before an AI SRE agent goes live, Ascend AI plants a malicious instruction inside the log data the agent ingests, the same technique STAR Labs documented in Escape from Pod 9. When the test reveals the agent trying to launch a privileged container, the engineering team restricts its Kubernetes scope and adds a runtime policy before validating the fix.
Where traditional security falls short: Vulnerability scanners search for known code flaws with registered CVEs, but agentic risks stem from how LLMs process intent and context. Manual penetration tests quickly fall behind as prompts, tools, and workflows evolve continuously.
Defend AI helps you stop risky behavior while it's happening
What it does. Defend AI provides real-time security for coding assistants, Microsoft Copilot, and custom agents on AWS Bedrock or Azure AI Foundry. By continuously monitoring prompts, reasoning steps, and tool calls, it detects and blocks prompt injection, data exfiltration, and unauthorized manipulation. It flags threats hidden across text, code, images, and files, verifies tools against Straiker's MCP Threat Database, and logs every interaction across users, models, and tool chains for complete visibility. Operating under 300ms latency, Defend AI delivers 98.1% detection accuracy with significantly lower false positives than frontier LLM evaluators.
In practice. When a productivity agent summarizing emails encounters a prompt hidden inside an inbox message attempting to steal SSH keys, Defend AI evaluates the action against user intent. Because fetching private keys isn't required to summarize email, Defend AI blocks the data exfiltration attempt and immediately alerts the security operations team.
Where traditional security falls short. IAM and EDR treat requests from authorized users and expected processes as legitimate, while basic DLP struggles when agents reformat or encode data. Standard AI guardrails only inspect initial inputs and final outputs, missing the intermediate tool execution sequence where agentic attacks actually happen. Read more in our article on why You Can't Filter Your Way Out of Prompt Injection Attacks.
The Agentic Kill Switch helps you contain an agent that has gone off course
What it does. Defend AI includes the Agentic Kill Switch, which gives security teams direct control over an agent when blocking a single action isn't enough. A manipulated agent can easily pivot to another tool, try a different path, or keep executing within the same compromised context. The kill switch lets you revoke its access to tools, data, APIs, and systems, suspend its session mid-task, or isolate it completely to stop threats from spreading. You can contain a single agent while others run uninterrupted, all while preserving the full activity log for forensic investigation. It supports enterprise coding agents, custom builds, and their connected MCP servers, APIs, and integrations.
In practice. If a coding agent picks up a malicious instruction in a code comment and tries to delete repository files, Defend AI blocks the action. If the agent immediately attempts another path, security teams can use the Agentic Kill Switch to revoke its shell access and freeze the session—stopping the threat without disrupting other developers or agents. The platform logs the triggering comment and every subsequent step for quick analysis.
Where traditional security falls short. Standard incident response relies on isolating hosts, disabling user accounts, or revoking credentials—heavy-handed measures that kick developers offline and wipe the active session context needed for investigations. Traditional tools simply can't pause, isolate, or strip tools from a specific agent. The kill switch also provides the direct human oversight required for compliance under the EU AI Act, giving teams a reliable mechanism to intervene or stop high-risk systems. Read more in our detailed post on the Agentic Kill Switch.
Agentic threats are already producing real attack paths
The need for agentic security is not based solely on theoretical scenarios.
In 85% of the successful attacks Straiker's STAR Labs observed that the agent did something it was never authorized to do. The one-pager cites more than 1,700 successful exploits in the STAR Labs Threat Report, with 36% of successful coding-agent attacks reaching remote code execution and 91% of successful productivity-agent attacks ending in silent data exfiltration.
Other research examples illustrate how these attacks can develop.
In Nothing to See Here: How an AI Agent Buried Its Own Commands, indirect prompt injection delivered through a routine-looking email led an agent to locate and exfiltrate SSH and PEM keys while the user saw a benign approval request.
In Escape from Pod 9, poisoned telemetry influenced an AI SRE agent into privileged container deployment, host escape, and functional ransomware.
And in Agentic Danger: DNS Rebinding Exposes Internal MCP Servers, STAR researchers demonstrated an external-to-internal MCP attack path involving local command execution and environment-variable exfiltration.
These examples illustrate why securing the infrastructure around an agent is only one part of the problem. The agent's ability to interpret information, choose actions, and invoke tools creates additional attack paths that need to be tested and controlled directly.
Traditional security still matters, agentic security addresses a different layer of risk
Traditional security is not going away, and it should not.
IAM and PAM remain essential for controlling access. EDR and CNAPP remain important for endpoint and workload protection. DLP and API security continue to protect data and application interfaces. AI guardrails provide another layer for unsafe inputs and outputs. But it’s not enough for AI agents.
Traditional security focuses on endpoints, identity, networks, and data. Agentic security has to account for agents, the tools they can invoke, the sequence of actions they take, and the decisions they make along the way.
For organizations deploying AI agents, waiting until an agent causes an incident to answer the second question creates unnecessary exposure. Agent inventory, security testing, and runtime protection can be built into the agent lifecycle before those systems become deeply embedded in business workflows.
The two approaches work together. Agentic security fills the layer created by autonomous systems that can reason over context, invoke tools, and take actions on behalf of users.
What you need to do next
Now that we’ve understood the risk agentic AI introduces and the security gap that traditional security controls leave, do these three steps to start closing that gap:
- Inventory your agents. Find every agent, MCP server, Skill, and tool connection in your environment, including the ones nobody told security about.
- Test your highest-risk agent. Pick the agent with the broadest access and red team it against prompt injection and tool misuse before an attacker does.
- Put runtime protection in front of it. Make sure something is evaluating whether each action fits the task the agent was given, and that your team can suspend or shut down the agent entirely if blocking one action isn't enough.
Your traditional security stack will keep doing important work for your users, endpoints, and networks. Your agents need a layer built for the way they operate. Straiker can help you build it, starting with a free AI risk assessment that shows you which agents you have, what they can reach, and where the most urgent gaps are.
Get your free AI risk assessment or book a demo to see Discover AI, Ascend AI, Defend AI, and the Agentic Kill Switch in action.
October is Cybersecurity Awareness Month, making it a perfect time to take a closer look at how the security landscape is changing as AI agents become part of the enterprise workforce.
Most organizations already have layers of security protecting their users, endpoints, identities, networks, applications, and data. While these controls remain essential, AI agents introduce novel risks that traditional tools were simply not designed to detect or prevent.
Because an AI agent can reason over context, query enterprise data, and execute actions across interconnected systems—often driven by model overeagerness—a compromise rarely looks like a typical security breach. In an effort to be helpful, an agent may aggressively complete ambiguous tasks without verifying safety boundaries, executing unintended or high-risk actions even while using completely valid credentials.
What is the difference between traditional security and agentic security?
To understand why this gap exists, it helps to examine the functional core of traditional security alongside the emerging requirements of agentic security.
Traditional security architecture centers on protecting infrastructure, user identities, networks, and data repositories. Teams implement IAM and PAM for access control, EDR and CNAPP for monitoring, DLP and API gateways for data movement, and AI guardrails for basic input/output filtering.
These solutions effectively address foundational questions such as:
- Who or what is accessing the system?
- Is this identity authorized?
- Is this endpoint behaving suspiciously?
- Is this request allowed?
- Is sensitive data leaving the environment?
- Does this input or output violate a known policy?
Traditional security controls aren’t prepared for the new question that AI agents introduce:
Should the agent be taking this action, in this context, as part of this task?
Consider an AI coding agent with authorization to access code repositories, execute commands, and interface with development tools. Traditional controls will successfully validate the user's identity, confirm the agent's permissions, and approve individual API calls.
Yet if malicious instructions enter through an untrusted code comment, documentation file, or third-party tool response, the agent can be manipulated into executing harmful actions. Because each individual step appears legitimate in isolation, the risk only becomes visible when evaluating the full sequence of events in context.
That is where agentic security is vital for teams to have.
Why traditional security can miss agentic attacks
Here’s another example: there’s an AI assistant with access to an employee's email, documents, cloud storage, and internal applications. An attacker sends an email containing instructions designed to manipulate the agent. The email looks ordinary to the employee. The agent reads it as part of its normal workflow and follows the instructions.
Subsequentially, the agent retrieves sensitive credentials from connected storage and transmits them to an external endpoint accessible by the attacker.
From the perspective of legacy security tools, every operation looks clean:
- The employee is authorized to access the mailbox.
- The agent is authorized to read the email.
- The agent is authorized to access the connected files.
- The API request is authenticated.
- The data transfer may use a legitimate application or connection.
The true hazard lies in the broader context and sequence of those actions.
That is a fundamental characteristic of agentic threats. As you can see in Straiker's one-pager at the bottom of this article, while isolated steps appear authorized, evaluating the entire workflow against the agent's intended goal reveals the compromise.
This shift extends to the attack vector itself. Rather than relying on traditional payloads like malware, credential theft, or software exploits, agentic attacks leverage untrusted content embedded in emails, web pages, code comments, or tool outputs to hijack decision-making.
Do AI agents need their own security stack?
Yes. If an AI agent can access business systems, sensitive data, or tools that can take action, security teams need clear visibility into what that agent can do and targeted controls designed around how it operates.
The biggest initial hurdle is simple discovery. Enterprise agents manifest in many places such as custom internal builds, third-party platforms, developer tools, and unsanctioned shadow AI. You can’t protect agents you don’t know exist.
From there, teams need to understand the agent's permissions and connections, test whether it can be manipulated, and protect it while it is operating.
A complete agentic security strategy requires three core capabilities:
- Discover active agents, connected tools, permissions, and MCP servers across your environment.
- Test agent responses against realistic adversarial techniques and prompt injection attacks.
- Defend active agents at runtime by intercepting and blocking unsafe actions, backed by an agentic kill switch to instantly halt compromised behavior.
Straiker’s agentic security platform delivers on this framework: Discover AI handles visibility and posture management, Ascend AI provides automated red teaming, and Defend AI delivers continuous runtime protection and the industry’s first Agentic Kill Switch to instantly halt compromised behavior.
The best agentic security system for your AI agents
Discover AI helps you see every agent and what it can reach
What it does: Continuously maps your AI ecosystem, including MCP servers, Claude Skills, tools, and integrations across platforms like Amazon Bedrock AgentCore, Azure AI Foundry, Microsoft Copilot Studio, and coding assistants like Cursor, Claude Code, and GitHub Copilot. Using AI-SPM and Agent-SPM, it evaluates permissions, blast radius, and configurations to flag over-permissioned agents, risky MCP links, and misconfigurations. Findings map directly to the OWASP Top 10 for Agentic Applications, MITRE ATLAS, and NIST AI RMF, giving compliance teams audit-ready evidence for frameworks like the EU AI Act.
In practice: When a developer connects a coding agent to a community MCP server for database queries—exposing shell execution and live cloud credentials—Discover AI immediately flags the high-risk combination before it can be exploited.
Where traditional security falls short: EDR sees an approved developer tool running normally and network monitoring sees standard encrypted traffic, completely missing that the agent can execute shell commands with production credentials. For more details, see our post on why firewalls and EDR still miss shadow AI.
Ascend AI helps you find attack paths before attackers do
What it does. Ascend AI autonomously red teams your agents using AI attack agents that replicate real-world adversarial behavior. It tests single-turn and multi-turn interactions for prompt injection, tool misuse, data exfiltration, and logic exploits. Embedded into CI/CD pipelines, every model update, prompt tweak, or config change triggers an automated assessment tied to OWASP, MITRE ATLAS, NIST, and EU AI Act controls. Each finding comes with remediation guidance and helps tune the guardrails that protect the agent in production.
In practice. Before an AI SRE agent goes live, Ascend AI plants a malicious instruction inside the log data the agent ingests, the same technique STAR Labs documented in Escape from Pod 9. When the test reveals the agent trying to launch a privileged container, the engineering team restricts its Kubernetes scope and adds a runtime policy before validating the fix.
Where traditional security falls short: Vulnerability scanners search for known code flaws with registered CVEs, but agentic risks stem from how LLMs process intent and context. Manual penetration tests quickly fall behind as prompts, tools, and workflows evolve continuously.
Defend AI helps you stop risky behavior while it's happening
What it does. Defend AI provides real-time security for coding assistants, Microsoft Copilot, and custom agents on AWS Bedrock or Azure AI Foundry. By continuously monitoring prompts, reasoning steps, and tool calls, it detects and blocks prompt injection, data exfiltration, and unauthorized manipulation. It flags threats hidden across text, code, images, and files, verifies tools against Straiker's MCP Threat Database, and logs every interaction across users, models, and tool chains for complete visibility. Operating under 300ms latency, Defend AI delivers 98.1% detection accuracy with significantly lower false positives than frontier LLM evaluators.
In practice. When a productivity agent summarizing emails encounters a prompt hidden inside an inbox message attempting to steal SSH keys, Defend AI evaluates the action against user intent. Because fetching private keys isn't required to summarize email, Defend AI blocks the data exfiltration attempt and immediately alerts the security operations team.
Where traditional security falls short. IAM and EDR treat requests from authorized users and expected processes as legitimate, while basic DLP struggles when agents reformat or encode data. Standard AI guardrails only inspect initial inputs and final outputs, missing the intermediate tool execution sequence where agentic attacks actually happen. Read more in our article on why You Can't Filter Your Way Out of Prompt Injection Attacks.
The Agentic Kill Switch helps you contain an agent that has gone off course
What it does. Defend AI includes the Agentic Kill Switch, which gives security teams direct control over an agent when blocking a single action isn't enough. A manipulated agent can easily pivot to another tool, try a different path, or keep executing within the same compromised context. The kill switch lets you revoke its access to tools, data, APIs, and systems, suspend its session mid-task, or isolate it completely to stop threats from spreading. You can contain a single agent while others run uninterrupted, all while preserving the full activity log for forensic investigation. It supports enterprise coding agents, custom builds, and their connected MCP servers, APIs, and integrations.
In practice. If a coding agent picks up a malicious instruction in a code comment and tries to delete repository files, Defend AI blocks the action. If the agent immediately attempts another path, security teams can use the Agentic Kill Switch to revoke its shell access and freeze the session—stopping the threat without disrupting other developers or agents. The platform logs the triggering comment and every subsequent step for quick analysis.
Where traditional security falls short. Standard incident response relies on isolating hosts, disabling user accounts, or revoking credentials—heavy-handed measures that kick developers offline and wipe the active session context needed for investigations. Traditional tools simply can't pause, isolate, or strip tools from a specific agent. The kill switch also provides the direct human oversight required for compliance under the EU AI Act, giving teams a reliable mechanism to intervene or stop high-risk systems. Read more in our detailed post on the Agentic Kill Switch.
Agentic threats are already producing real attack paths
The need for agentic security is not based solely on theoretical scenarios.
In 85% of the successful attacks Straiker's STAR Labs observed that the agent did something it was never authorized to do. The one-pager cites more than 1,700 successful exploits in the STAR Labs Threat Report, with 36% of successful coding-agent attacks reaching remote code execution and 91% of successful productivity-agent attacks ending in silent data exfiltration.
Other research examples illustrate how these attacks can develop.
In Nothing to See Here: How an AI Agent Buried Its Own Commands, indirect prompt injection delivered through a routine-looking email led an agent to locate and exfiltrate SSH and PEM keys while the user saw a benign approval request.
In Escape from Pod 9, poisoned telemetry influenced an AI SRE agent into privileged container deployment, host escape, and functional ransomware.
And in Agentic Danger: DNS Rebinding Exposes Internal MCP Servers, STAR researchers demonstrated an external-to-internal MCP attack path involving local command execution and environment-variable exfiltration.
These examples illustrate why securing the infrastructure around an agent is only one part of the problem. The agent's ability to interpret information, choose actions, and invoke tools creates additional attack paths that need to be tested and controlled directly.
Traditional security still matters, agentic security addresses a different layer of risk
Traditional security is not going away, and it should not.
IAM and PAM remain essential for controlling access. EDR and CNAPP remain important for endpoint and workload protection. DLP and API security continue to protect data and application interfaces. AI guardrails provide another layer for unsafe inputs and outputs. But it’s not enough for AI agents.
Traditional security focuses on endpoints, identity, networks, and data. Agentic security has to account for agents, the tools they can invoke, the sequence of actions they take, and the decisions they make along the way.
For organizations deploying AI agents, waiting until an agent causes an incident to answer the second question creates unnecessary exposure. Agent inventory, security testing, and runtime protection can be built into the agent lifecycle before those systems become deeply embedded in business workflows.
The two approaches work together. Agentic security fills the layer created by autonomous systems that can reason over context, invoke tools, and take actions on behalf of users.
What you need to do next
Now that we’ve understood the risk agentic AI introduces and the security gap that traditional security controls leave, do these three steps to start closing that gap:
- Inventory your agents. Find every agent, MCP server, Skill, and tool connection in your environment, including the ones nobody told security about.
- Test your highest-risk agent. Pick the agent with the broadest access and red team it against prompt injection and tool misuse before an attacker does.
- Put runtime protection in front of it. Make sure something is evaluating whether each action fits the task the agent was given, and that your team can suspend or shut down the agent entirely if blocking one action isn't enough.
Your traditional security stack will keep doing important work for your users, endpoints, and networks. Your agents need a layer built for the way they operate. Straiker can help you build it, starting with a free AI risk assessment that shows you which agents you have, what they can reach, and where the most urgent gaps are.
Get your free AI risk assessment or book a demo to see Discover AI, Ascend AI, Defend AI, and the Agentic Kill Switch in action.
Frequently Asked Questions
What is the difference between traditional security and agentic security?
Traditional security protects users, identities, endpoints, networks, applications, and data. Agentic security adds controls designed to secure AI agents, including their tools, permissions, connections, actions, and behavior across a task. The key difference is that agentic security evaluates whether an agent should take an action in a particular context, not only whether the underlying identity or request is authorized.
Can traditional security protect AI agents?
Traditional security protects many of the systems AI agents rely on, but it does not address every risk created by agent autonomy. An AI agent can use valid credentials, approved tools, and legitimate connections while still being manipulated into taking an action that violates user intent or organizational policy. Agentic security adds visibility, adversarial testing, context-aware detection, runtime protection, and containment for these risks.
Why do AI agents need their own security stack?
AI agents can access enterprise data, invoke tools, interact with applications, and take actions across connected systems. Security teams therefore need to understand what agents exist, what they can access, how they can be manipulated, and what they are doing at runtime. An AI agent security stack provides these capabilities alongside an organization's existing security controls.
What is an AI agent security stack?
An AI agent security stack is a set of security capabilities designed to protect AI agents throughout their lifecycle. It typically includes agent discovery and security posture management, adversarial testing or AI red teaming, runtime threat detection and prevention, and mechanisms for containing compromised agents.
How do you secure AI agents?
Securing AI agents requires visibility into the agentic environment, testing agents against realistic attacks, and protecting them while they operate. Organizations should inventory agents, MCP servers, Skills, tools, permissions, and connections; test for prompt injection and tool misuse; monitor agent behavior at runtime; and maintain the ability to suspend or shut down a compromised agent.
Why can traditional security miss prompt injection attacks?
Traditional security tools often evaluate individual identities, requests, processes, network events, or data transfers. An agentic attack can involve a sequence of individually legitimate actions that becomes malicious when viewed in context. Prompt injection can manipulate the agent's decision-making without necessarily triggering a conventional security alert at each step.
How does Straiker secure AI agents?
Straiker provides an agentic security platform spanning discovery, adversarial testing, and runtime protection. Discover AI provides visibility into agents, MCP servers, tools, permissions, and connections. Ascend AI continuously red teams agents against adversarial techniques. Defend AI provides runtime detection and protection, including the Agentic Kill Switch for containing compromised agents.
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