Introduction
Employees are no longer working alone. They are increasingly collaborating with AI copilots, assistants and autonomous agents that help write code, summarize incidents, analyze data and make business decisions.
This shift is happening fast. Goldman Sachs estimates agentic AI could represent roughly 60% of software market value by 2030.
But as AI adoption accelerates, so does risk. Traditional security tools were built to protect systems, networks and endpoints. Today, the real security challenge lives in that interaction layer, where employees trust AI outputs, AI agents access sensitive data and attackers exploit both sides of the relationship.
To secure this new hybrid workforce, organizations need more than traditional controls. They need a security strategy built specifically for AI agents and the humans working alongside them. This strategy revolves around four core pillars: Discover, Monitor, Detect and Protect. Together, these pillars, alongside the right security capabilities, provide the visibility, control and resilience needed to reduce risk across your human/AI workforce.
Pillar 1: Discover
See Every AI Agent in Your Environment — Including the Ones You Didn’t Approve
The first step in securing a hybrid workforce is visibility. You cannot protect what you cannot see, and in today’s workplace, that includes a growing number of AI agents operating across your environment without formal security oversight.
Employees are using internet browsers, Microsoft Copilot, ChatGPT, Google Gemini, Claude and other AI tools to write content, analyze data, summarize meetings and automate decisions. Some are approved by IT. Many are not.
This creates a new version of an old problem: shadow AI. Just like shadow IT, employees adopt AI tools because they help them work faster. But unlike traditional apps, AI agents can access sensitive data, trigger workflows and influence business decisions, often without the visibility security teams rely on from traditional controls like SIEM, DLP or endpoint monitoring.
Before CISOs can govern AI usage, enforce policy or reduce risk, they need to answer some basic questions:
- What AI agents are operating in the environment?
- Who is using them?
- What tools and data can they access?
- Which agents create the biggest potential blast radius?
KnowBe4 Agent Risk Manager
Agent Risk Manager provides instant, zero-configuration discovery of every AI agent operating across your environment. This includes automatic identification of:
- Enterprise AI platforms like Microsoft Copilot, OpenAI ChatGPT, Google Gemini and Anthropic Claude
- Approved agents deployed through official workflows
- Unsanctioned Shadow AI introduced without security review
Agent Risk Manager also includes a Tool Network view that visually maps:
- How agents connect to enterprise tools
- Which tools are shared across multiple agents
- Where the highest potential blast radius exists if an agent is compromised This shows where AI risk is concentrated and where attention is needed most.
What This Means for Security Leaders
Instead of discovering shadow AI after a compliance issue, data leak or security incident, security teams gain continuous, real-time visibility into every agent and every connection point.
This allows security teams to move from reactive cleanup to proactive governance. You can identify risk earlier, understand exposure faster and apply policy before a problem becomes a breach.
It also changes the conversation around AI adoption. The goal is not to stop employees from using AI, as that battle is already over. The goal is to make AI usage visible, governed and secure.
When AI agents are treated like any other enterprise identity—with oversight, accountability and clear boundaries—organizations can support innovation without sacrificing control.
Pillar 2: Monitor
Capture Every Interaction Across Humans and AI Agents
In a hybrid workforce, the real security challenge is not just who can use AI, but how employees and AI agents work together once access is granted. Prompts, responses, tool calls and automated actions all create new opportunities for data exposure, policy violations and manipulation.
SIEMs, DLP platforms and endpoint controls were not built to monitor prompt-level behavior or understand how AI agents make decisions. They can show that an action happened, but not why it happened or how an AI agent was influenced to take it. That creates a security blind spot.
CISOs need visibility into the full interaction layer where humans rely on AI to summarize information, make recommendations and trigger actions across the business. Without that visibility, investigating incidents becomes guesswork.
You need to know:
- What prompt triggered the action
- What the AI agent returned
- What tools it accessed
- Whether the behavior aligned with policy
KnowBe4 Agent Risk Manager Capabilities
Agent Risk Manager provides a complete audit trail down to the individual conversation level. It captures:
- User actions
- Prompts and AI responses
- Tool invocations
- Detection triggers
- Schema discoveries
- Security events across the full interaction lifecycle
From a user’s initial request to the final agent action, every step is logged with the context needed for investigation. Agent Risk Manager also provides a centralized monitoring dashboard with real-time activity feeds, giving analysts immediate visibility into how AI agents are being used across the organization.
What This Means for Security Leaders
Agent Risk Manager gives CISOs end-to-end visibility into the interaction layer. Instead of treating AI as a black box, security teams can see exactly how decisions were made and where risk entered the process.
This means you can:
- Trace decisions back to specific prompts
- Understand how AI is being used in real workflows
- Identify misuse before it becomes a larger issue
- Investigate incidents with full context, not partial logs
That level of visibility is critical for both security and compliance, in addition to supporting faster investigations and clearer accountability across both human and AI activity.
Most importantly, it helps security teams answer one of the hardest questions in AI security: “Why did this happen?”
Pillar 3: Detect
Identify Risky Behavior and AI-Specific Threats in Real Time
Traditional security tools were not built for prompt injection, agent overstepping or AI systems making the wrong decision with complete confidence. That is the challenge of the hybrid workforce.
Many AI-related cyber incidents do not look like traditional cyberattacks. There may be no malware, no phishing link and no credential theft. Instead, the risk happens inside the workflow: an employee enters a prompt, an AI agent accesses the wrong data or a malicious input quietly manipulates the outcome.
This is why AI security requires a new detection model. CISOs need visibility into behavioral risk, not just technical compromise. Security teams must be able to detect when an AI agent is being manipulated, when a user is taking unsafe actions or when trusted systems begin operating outside policy. Detection must move closer to the decision itself.
KnowBe4 Agent Risk Manager Capabilities
Agent Risk Manager includes six purpose-built detection engines designed specifically for major AI agent attack categories.
These include:
- Prompt Injection
- Sensitive Information Exposure (PII, passwords, credentials)
- Unbounded Consumption (resource abuse and excessive API usage)
- Content Safety Violations
- Privilege Escalation
- Agent Overstepping
Its real-time Detection Center gives analysts a live feed of every risky event, categorized by severity and threat type, so teams can quickly understand where active risk exists. Agent Risk Manager also provides:
- User Risk Scoring to identify individuals whose AI interactions create elevated risk
- Holistic Risk Scoring that combines human and AI behavior into a single risk profile for better prioritization
This allows security teams to focus on the users, agents and workflows that matter most.
What This Means for Security Leaders
Agent Risk Manager delivers AI-native threat detection, not repurposed legacy controls trying to fit a new problem. It helps security teams detect the kinds of attacks and incidents traditional tools often miss:
- Prompt manipulation instead of malware
- Unauthorized actions instead of stolen credentials
- Risky behavior inside trusted workflows instead of attacks at the perimeter
- Attacks that never trigger traditional alerts
- Manipulation at the prompt and behavior level
- Allows security teams to prioritize investigations based on real user and agent activity
- Organizations reduce exposure before risky behavior becomes a breach
Pillar 4: Protect
Act in Real Time to Reduce Risk and Change Behavior
In a hybrid workforce, security teams cannot afford to wait until after an incident to respond. When employees and AI agents are working together, protection must happen at the moment risk appears.
This is especially important because many AI-driven security events are not obvious breaches. They are small, high-risk decisions happening inside normal workflows: an employee pastes sensitive data into an AI prompt, an agent attempts to access data outside its scope or a malicious prompt influences an AI response.
By the time a traditional alert is reviewed, the damage may already be done, which is why visibility and detection are key and where security shifts from reactive response to active risk reduction.
KnowBe4 Agent Risk Manager Capabilities
Agent Risk Manager provides real-time threat interception and blocking, allowing security teams to stop risky actions before they create business impact.
When a high-risk event occurs, Agent Risk Manager can:
- Block the operation
- Trigger an alert
- Log the full event for investigation
- Deliver immediate contextual coaching to the user
That coaching explains:
- Why the action was risky
- What policy was violated
- How to make a safer decision moving forward
This matters because behavior change is the real goal. KnowBe4 data shows that 70% of users who receive real-time coaching never repeat the same risky behavior, creating permanent risk reduction over time.
Agent Risk Manager also supports:
- Policy enforcement and governance controls
- Prevention of unauthorized actions
- Enforcement of agent role boundaries
- Blast radius analysis to understand and contain the impact of compromised tools or agents
- An “outside-in” control layer that secures AI systems without modifying the underlying models
This gives organizations strong security controls without disrupting productivity or requiring changes to AI providers.
What This Means for Security Leaders
Instead of simply notifying security teams after risky behavior occurs, Agent Risk Manager helps prevent the issue in real time and enables security teams to:
- Stop threats before they become incidents
- Reduce repeat risk through proven behavior change
- Enforce policy across both humans and AI agents
- Contain the impact of compromised tools or unsafe actions
This is where modern security becomes adaptive and resilient while continuously improving how people and AI work together safely.
Conclusion
Securing the hybrid workforce now requires managing the relationship between humans and AI agents. As AI becomes a core operational participant, trust must be backed by visibility and governance.
To innovate safely and remain resilient, CISOs must look beyond traditional defense toward a four-pillar strategy:
- Discover - Eliminate AI blind spots.
- Monitor - Understand human-agent interactions.
- Detect - Identify risky behavior in real time.
- Protect - Stop threats before they escalate.
The organizations that embrace this reality—and secure both sides of the workforce—will be best positioned to stay resilient in the AI era.