Building Trust With Agentic AI From Pindrop: AI Security Explained

Building trust with agentic AI from Pindrop means creating secure and reliable ways for businesses to verify AI agents, human users, and their actions. As AI agents become more capable of making decisions and completing tasks, the risk of deepfakes, voice cloning, impersonation, and automated fraud also increases. Pindrop focuses on technologies such as voice authentication, fraud detection, deepfake detection, and identity verification to help organizations determine whether an interaction is genuine and whether the right person or AI agent is authorized to act. In simple terms, the goal is to make AI-powered interactions more secure, trustworthy, and accountable while still allowing businesses to benefit from automation.

What Is Agentic AI?

Agentic AI refers to artificial intelligence systems that can perform tasks with a greater level of autonomy.

Traditional AI often responds to a prompt. For example, you might ask an AI chatbot to summarize a document, and it produces the summary.

An AI agent can go further. Depending on how it is designed and authorized, it may:

• Understand a goal
• Plan multiple steps
• Use software tools
• Access information
• Make decisions within defined limits
• Communicate with other systems
• Complete tasks on behalf of a user
• Take actions without requiring constant human input

For businesses, this could mean AI agents handling customer service requests, administrative tasks, scheduling, insurance workflows, transactions, or other repetitive processes.

The benefit is greater automation. The challenge is determining when an AI agent should be trusted to act.

Why Trust Matters in Agentic AI

The more autonomy an AI system receives, the more important security and authorization become.

Imagine an AI agent that is allowed to help a customer resolve an account issue. If the agent is legitimate and properly authorized, automation could make the experience faster.

Now imagine an attacker creates a fake AI agent that impersonates a legitimate system.

The situation becomes much more dangerous.

An attacker could potentially use AI-powered automation to:

• Impersonate customers or employees
• Generate synthetic voices
• Create convincing video impersonations
• Automate social engineering
• Attempt fraudulent transactions
• Manipulate customer-service interactions
• Exploit authentication systems
• Scale attacks much faster than a human attacker

Pindrop describes this changing environment as a new trust challenge. Its Chief Product Officer, Nicholas Holland, frames the future around three questions: Is this the right entity? Does it have the right intent? And are we enabling the right action?

That is a useful way to understand why traditional identity checks may not be enough for agentic systems.

Key Takeaways

• Agentic AI can perform tasks and make decisions with less human intervention.
• Greater autonomy creates greater security and authorization risks.
• AI-generated voices and videos can make impersonation more convincing.
• Trust should involve more than simply checking credentials.
• Pindrop emphasizes verifying both AI-agent identity and human authenticity.
• Real-time signals can help organizations make better risk decisions.
• Human approval becomes particularly important when an AI agent is authorized to perform high-impact actions.
• Businesses should combine AI automation with appropriate security, governance, monitoring, and human oversight.

How Pindrop Views Trust in the Age of AI

Pindrop describes itself as a trust layer for real-time communications.

Its approach is particularly relevant because many fraud and authentication decisions happen during live interactions.

For example, a customer may call a contact center, an employee may join a video meeting, or an organization may interact with an automated system.

In these situations, simply asking whether someone has the correct credentials may not provide enough information.

Pindrop’s current thinking places increasing emphasis on questions such as:

1. Is this a real human or a machine?
2. Is this interaction legitimate or malicious?
3. Is this the correct person or entity?
4. Is the requested action authorized?

Pindrop describes its broader approach as combining “Real Human” and “Right Human” concepts, meaning organizations need to determine both whether an interaction is genuinely human and whether it comes from the authorized person.

The Difference Between Agent Identity and Human Identity

One of the most important ideas in building trust with agentic AI is that AI-agent identity and human identity are connected but different problems.

Suppose an employee authorizes an AI agent to perform a task.

The organization may need to answer two questions:

• Is this the legitimate AI agent?
• Did the legitimate employee actually authorize the action?

Checking only the first question leaves a major gap.

An attacker could potentially compromise or manipulate the human approval process. The AI agent could technically be legitimate, but the authorization behind its action could still be fraudulent.

Pindrop argues that human approval integrity is therefore an important part of enterprise AI security.

Why Human Approval Matters

AI agents often act on behalf of people.

That means an important action can have a chain of authorization:

Human → AI Agent → System → Action

If the human authorization is fake, compromised, or obtained through impersonation, the entire chain becomes questionable.

For example, consider a financial workflow.

An employee approves an action through a voice or video interaction. An AI agent then uses that approval to complete the next steps.

If an attacker can impersonate the employee using synthetic media, the downstream AI agent might perform a technically valid action based on an illegitimate approval.

This is why Pindrop says organizations need to consider human authenticity alongside agent identity and authorization.

AI Deepfakes Are Changing the Trust Problem

Generative AI has made synthetic media increasingly convincing.

A deepfake can involve manipulated or AI-generated:

• Voice
• Video
• Images
• Audio recordings
• Digital identities

Voice cloning is particularly important for telephone-based fraud because people traditionally rely heavily on what they hear.

Pindrop has highlighted the increasing use of AI-generated voices in scams and impersonation attacks, including attacks involving contact centers.

The problem is simple:

If a voice can be artificially created, hearing a familiar voice is no longer enough to establish trust.

The same principle applies to video.

A person appearing on a video call may look legitimate while the underlying media has been manipulated or generated.

Why Traditional Authentication May Not Be Enough

Authentication remains important, but modern AI attacks demonstrate why organizations should avoid relying on a single signal.

For years, organizations have used methods such as:

• Passwords
• PINs
• Security questions
• Voice authentication
• One-time codes
• Biometrics
• Device authentication

These controls can still be useful.

However, Pindrop argues that organizations increasingly need to determine whether an interaction is genuine before relying on identity signals. Its “real human first, right human second” concept reflects this change.

The basic idea is:

First determine whether the interaction is authentic. Then determine whether the person is authorized.

This layered approach can be particularly useful when AI-generated impersonation is involved.

How Pindrop Can Help Build Trust With Agentic AI

Pindrop’s approach focuses on analyzing interactions and identifying signals associated with fraud, impersonation, and synthetic media.

The company says its technology is designed to help organizations detect deepfakes, authenticate real people, and establish trust across real-time communications.

Its broader platform includes technologies and products focused on areas such as:

• Voice authentication
• Fraud detection
• Deepfake detection
• Video security
• Digital identity
• Risk assessment
• Investigation support

Pindrop also describes using agentic AI internally within security workflows. For example, its Fraud Assist technology helps analysts review calls, summarize activity, and accelerate fraud investigations. The company says it is expanding these capabilities toward more advanced security-focused agents that can analyze patterns across interactions and identify emerging fraud trends.

Pindrop’s Two-Sided Approach to Agentic AI Security

An interesting part of Pindrop’s strategy is that it considers agentic AI from two directions.

Protecting Against Malicious AI Agents

AI agents can be used by attackers.

A malicious agent could automate interactions, impersonate people, probe systems, or attempt to manipulate customer-service processes.

Businesses therefore need mechanisms to identify suspicious automated activity and distinguish legitimate agents from malicious ones.

Using AI Agents for Security

AI can also become part of the defense.

Security-focused AI agents can help analysts investigate large volumes of interactions, connect signals, identify unusual patterns, and prioritize potential threats.

Pindrop describes this as a complementary approach: defending organizations against increasingly sophisticated AI-driven threats while also using agentic AI to improve fraud detection and investigation.

This creates an important security principle:

AI can be both part of the threat and part of the defense.

Continuous Trust Is More Important Than One-Time Verification

Traditional security often treats authentication as an event.

A user logs in, provides credentials, and receives access.

Agentic AI introduces a more complicated situation because the system may continue making decisions after the original authorization.

This raises questions such as:

• Is the agent still behaving as expected?
• Has the risk level changed?
• Is the action within the agent’s permissions?
• Has the user’s authorization changed?
• Is the interaction still genuine?
• Should the system request additional verification?

This is why continuous trust can become more important than a single authentication event.

Pindrop describes its broader vision as creating a continuous trust layer across customer interactions and moving organizations toward more predictive identity intelligence.

The Role of Real-Time Risk Signals

A modern trust system can consider multiple signals instead of relying on one piece of information.

Depending on the environment, these could include:

• Identity information
• Device information
• Behavioral patterns
• Interaction characteristics
• Audio signals
• Video signals
• Context
• Transaction details
• Historical activity
• Known fraud indicators

Pindrop’s research argues that high-risk interactions can benefit from combining interaction-based signals with contextual and behavioral information when determining whether human approval is authentic.

The goal is not necessarily to make every interaction difficult.

Instead, the goal is to make security decisions more intelligent and context-aware.

Agentic AI and Customer Service

Customer service is one area where agentic AI could have a significant impact.

Businesses already use automated systems for:

• Customer questions
• Account support
• Appointment scheduling
• Information retrieval
• Claims processing
• Troubleshooting
• Transaction assistance

As these systems become more autonomous, they may gain access to more sensitive information and business processes.

That creates a trust requirement.

A company needs to know whether the person or AI system requesting an action is legitimate before allowing the agent to perform sensitive operations.

This is especially important for industries such as:

• Banking
• Insurance
• Healthcare
• Telecommunications
• E-commerce
• Government services

In these environments, a successful impersonation attempt could result in financial loss, privacy violations, or unauthorized access.

Building Trust Without Destroying User Experience

Security can sometimes create friction.

If customers have to complete multiple complicated verification steps for every interaction, they may become frustrated.

This creates a balancing act:

Strong security + low friction = better customer experience

Technologies that operate passively in the background can potentially help organizations assess risk without requiring users to repeatedly prove their identity.

Pindrop’s platform focuses on analyzing interactions and authentication signals in real time, with the goal of detecting threats while supporting smoother customer experiences.

The exact implementation will depend on an organization’s risk level, technology stack, regulations, and customer requirements.

What Businesses Should Consider Before Deploying Agentic AI

Agentic AI should not be deployed simply because it can automate a task.

Organizations should first establish clear security and governance rules.

1. Define What the Agent Can Do

Every AI agent should have clearly defined permissions.

For example, an agent may be allowed to:

• Read information
• Create a support ticket
• Schedule an appointment

But it may not be allowed to:

• Transfer large amounts of money
• Change sensitive account information
• Delete important records
• Approve high-risk transactions

Least-privilege principles can reduce the damage caused by compromised or misused agents.

2. Verify Human Authorization

When an AI agent acts on behalf of a person, organizations should consider how that person’s authorization is verified.

For high-risk operations, a simple voice instruction may not be enough.

Additional signals or verification steps may be appropriate.

3. Monitor Agent Behavior

Organizations should monitor AI agents after deployment.

Important questions include:

• What actions is the agent taking?
• Are those actions expected?
• Is the agent accessing unusual information?
• Are failure rates increasing?
• Is there suspicious activity?
• Does the agent need human intervention?

4. Prepare for Deepfake Attacks

Security teams should assume that attackers can use synthetic media.

That means voice and video should not automatically be treated as proof of identity.

Pindrop’s research emphasizes this changing threat landscape, particularly in real-time communications.

5. Keep Humans in the Loop for High-Risk Decisions

Not every AI decision needs human approval.

However, high-impact decisions may benefit from human oversight.

A useful approach is to establish clear thresholds.

For example:

Low risk → AI can act automatically

Medium risk → AI requests additional verification

High risk → AI escalates to a human

This can provide a practical balance between automation and security.

Is Pindrop’s Approach Only About Voice?

No.

Although Pindrop has strong roots in voice security and contact-center technology, its current strategy extends across voice, video, digital interactions, and emerging AI-driven experiences.

This matters because modern fraud is no longer restricted to telephone calls.

Attackers can move between:

• Phone calls
• Video meetings
• Websites
• Mobile applications
• Chat
• Digital services
• AI-powered interfaces

A trust strategy therefore needs to consider multiple channels.

The Future of Trust With Agentic AI

Agentic AI is likely to become increasingly integrated into business operations.

As agents become more capable, organizations will need to answer a fundamental question:

How do we know an AI agent should be trusted to perform this action right now?

The answer will probably not come from a single technology.

Instead, trust will involve multiple layers, including:

• Agent identity
• Human identity
• Human authenticity
• Authorization
• Context
• Behavioral signals
• Continuous monitoring
• Risk assessment
• Human oversight
• Auditability

Pindrop’s recent work reflects this broader shift from traditional identity verification toward continuous trust in real-time interactions.

Why Building Trust With Agentic AI Matters for the Future

Agentic AI could make businesses more efficient, but autonomy increases the consequences of mistakes.

A traditional chatbot that gives a wrong answer is inconvenient.

An autonomous AI agent that performs the wrong transaction or changes sensitive information can create a much more serious problem.

That is why trust must become part of AI architecture rather than an afterthought.

Pindrop’s perspective is particularly focused on the intersection between AI, identity, real-time communications, fraud, and synthetic media. Its approach highlights an important change in cybersecurity: organizations increasingly need to determine not only who is interacting with a system, but also whether the interaction itself is genuine.

Frequently Asked Questions

What does building trust with agentic AI from Pindrop mean?

Building trust with agentic AI from Pindrop refers to the idea of establishing reliable identity, authorization, authenticity, and risk controls around AI agents and the humans who authorize them. Pindrop focuses particularly on real-time communications, fraud detection, deepfake detection, and continuous identity verification.

What is agentic AI?

Agentic AI describes AI systems that can pursue goals, use tools, make decisions, and complete tasks with a greater degree of autonomy than traditional conversational AI.

Why is trust important for agentic AI?

Trust is important because AI agents may eventually perform sensitive actions on behalf of people and businesses. Organizations need to know whether the agent is legitimate, whether the authorization is genuine, and whether the requested action is appropriate.

Can AI agents be used for fraud?

Yes. Attackers can use AI-powered automation, synthetic voices, deepfakes, and other technologies to scale impersonation and fraud attempts. Pindrop has specifically highlighted the growing threat of AI-driven voice and video impersonation.

How does Pindrop approach AI security?

Pindrop focuses on detecting threats such as deepfakes and impersonation while authenticating legitimate people and interactions. The company also describes using agentic AI in security workflows to assist with fraud investigation and analysis.

Why isn’t AI-agent identity alone enough?

An AI agent can be legitimate while the person authorizing its actions is not. Pindrop argues that organizations therefore need to consider both agent identity and human approval integrity.

What is continuous identity verification?

Continuous identity verification means assessing trust throughout an interaction instead of relying only on a single authentication event at the beginning. It can use multiple signals to identify changes in risk.

Can deepfake detection stop every AI attack?

No security technology can guarantee that every attack will be detected. Deepfake detection should be considered one component of a broader security strategy that can include authentication, authorization, monitoring, access controls, and human oversight.

Conclusion

Building trust with agentic AI from Pindrop reflects a larger change in cybersecurity: AI is becoming capable of acting on behalf of humans, while attackers are simultaneously using AI to become better at impersonation and fraud.

That means organizations can no longer rely solely on traditional identity checks.

They need to consider whether an interaction is genuine, whether the person behind an authorization is authentic, whether the AI agent is legitimate, and whether the requested action is appropriate for the situation.

Pindrop’s approach emphasizes this broader trust model by combining identity, authenticity, fraud detection, deepfake detection, and AI-powered security capabilities across real-time communications.

For businesses adopting agentic AI, the most important lesson is simple:

AI should not only be capable of acting. It should be capable of acting within a trusted, verifiable, and well-governed environment.

As autonomous AI becomes more common, trust will become just as important as intelligence. The organizations that build strong identity, authorization, monitoring, and human-oversight systems alongside AI agents will be better prepared for the next stage of the AI era.