Implementing Agentic AI: Security and Ethical Considerations

Nearly half (49%) of technology leaders in PwC’s October 2024 Pulse Survey [1] said AI was “fully integrated” into their companies’ core business strategy. A third reported full integration into their products and services. According to McKinsey’s report [2], 92% of companies plan to increase their AI investments over the next three years — making AI security and ethics a growing priority across industries.

As AI agents become more embedded in core business functions, the stakes grow higher. With greater autonomy comes greater responsibility — for the agents and the organizations deploying these agents. Security and ethics are foundational pillars for building trust, ensuring compliance and avoiding unintended consequences.

At the UNIDIR Global Conference on AI, Security and Ethics 2025, UN Secretary-General António Guterres warned: “AI is profoundly reshaping how we live, work and communicate… But unregulated AI also presents unprecedented risks.”

In this post, we’ll explore the key security and ethical considerations leaders must address when implementing agentic AI.

Agentic AI and Its Role in the Evolution of Automation

Agentic AI isn’t just another step in the evolution of automation, it’s a leap. While traditional automation follows fixed scripts and narrow AI performs well-defined tasks within specific domains, agentic AI introduces a new paradigm: autonomy. These intelligent agents don’t just execute instructions, they can:

• Interpret objectives, 
• Make decisions, 
• Learn from context, 
• Coordinate with humans and other systems, 
• Adapt in real time, 
• Switch strategies when faced with obstacles, and 
• Delegate tasks to other agents — operating more like dynamic team members than static tools.

This shift opens the door to a radical transformation in how businesses function. Instead of manually orchestrating siloed systems or constantly toggling between apps, organizations can deploy agentic AI to handle complex, multi-step workflows across departments — from IT to HR to customer service. These agents can enhance the customer experience by delivering hyper-personalized, proactive support. They can boost operational efficiency by identifying inefficiencies and acting on them instantly.

As a result, organizations become faster, more responsive, and more resilient. But with this power comes the need for strong ethical and security foundations, because agents capable of autonomous action must also be designed to act responsibly.

Security Considerations for Agentic AI Implementations

The Forrester report “Global Commercial AI Software Governance Market Forecast, 2024 to 2030” [3] says that by 2030, spending on off-the-shelf AI governance software will more than quadruple compared to 2024 reaching $15.8 billion and capturing 7% of total AI software spend. As agentic AI takes on more autonomous responsibilities across organizations, security must evolve just as quickly. Anu Talus, the Chair of the European Data Protection Board (EDPB) emphasized: “AI technologies may bring many opportunities and benefits to different industries and areas of life. We need to ensure these innovations are done ethically, safely, and in a way that benefits everyone.”

Figure 1: Four Critical Areas to Secure Agentic AI Implementation

Data Protection & Privacy

Agentic AI systems often handle personally-identifiable information (PII) and other sensitive customer or employee data; one misstep in managing this data can lead to serious breaches or compliance violations. To safeguard privacy and build trust:

• Ensure end-to-end encryption and secure data handling practices.
• Comply with global privacy regulations, including General Data Protection Regulation (GDPR) [4] in EU, Organization for Economic Co-operation and Development (OECD) AI Principles [5], and others — not just to avoid penalties, but to maintain credibility.
• Implement data minimization and role-based access controls to reduce unnecessary exposure.

Access Controls & Identity Management

• Establish clear roles, permissions, and identity verification for every agent.
• Use multi-factor authentication (MFA) and zero-trust architectures to prevent unauthorized access.
• Log and audit every action taken by agents to maintain traceability and accountability.

Model Robustness & Adversarial Threats

• Guard against prompt injection attacks, hallucinations, and adversarial inputs that can mislead models.
• Stress-test your models regularly to identify vulnerabilities.
• Maintain version control and detailed records of model updates and decisions to ensure auditability.

System Monitoring & Incident Response

• Deploy real-time monitoring tools to detect anomalies in agent behavior.
• Create feedback loops to learn from incidents and improve defenses.
• Build rapid-response protocols to isolate and mitigate threats before they escalate.

Ethical Considerations for Agentic AI Implementations

As more agentic AI-led solutions are integrated into business processes, ethics is becoming a core design principle, and aligning AI with human values is mission-critical. From transparency to accountability, organizations must embed ethical checkpoints throughout the entire AI lifecycle, from design to decommissioning, to build AI solutions that people can trust.

Timothy Kang, Global AI Governance & Policy Leader and advisor to OpenAI, UNESCO, and NASA, states: “Trust is no longer a differentiator — it’s the infrastructure on which responsible AI innovation must be built. Without trust, AI can’t scale.”

Figure 2: Ethical Pillars for Agentic AI Deployments

Transparency & Explainability

• Decisions must be understandable to humans, especially in high-stakes environments, such as healthcare, finance, or HR.
• Implementing audit trails — clear logs of how and why decisions were made — ensures traceability and accountability.

Bias & Fairness

• Use bias detection tools, incorporate diverse datasets, and design with inclusion in mind to prevent harm.

Autonomy vs. Oversight

• Define “human-in-the-loop” (active supervision) vs. “human-on-the-loop” (passive oversight) depending on context and risk.
• Autonomy must align with your organization’s values, risk appetite, and ethical standards.
• Think of agents as employees: even the most capable need boundaries and reviews.

Responsibility & Accountability

• Establish clear ownership structures — whether that’s the AI team, business unit, or vendor.
• Embed ethical frameworks into development and deployment processes to guide decisions and prevent harm.

Building a Governance Framework

When it comes to deploying agentic AI responsibly, governance is essential. Remember you’re managing systems that can think, act, and learn. That means breaking down traditional silos and bringing together a diverse mix of minds: IT teams, legal and compliance experts, business leaders, and innovation champions who ensure you’re building solutions that are both powerful and secure.

An organization’s Agentic AI governance framework should include:

• Policy Development & Documentation

Set clear guidelines for agent behaviors, data access, and escalation protocols — document everything.

• Lifecycle Management & Version Control

AI agents evolve. Track versions, monitor updates, and retire what no longer serves your goals.

• Ethical Review Boards

Assemble cross-functional teams to vet new use cases, assess risks, and stress-test ethical boundaries before go-live.

• Regular Audits & Feedback Loops

Bake in continuous improvement. Set up recurring reviews to refine models, assess outcomes, and avoid ‘set-it-and-forget-it’ traps.

Practices and Questions to Ask Before Putting AI Agents to Work

Nearly 9 in 10 U.S. executives are giving agentic AI the green light to make decisions and complete tasks on behalf of their customers — and 47% are fully comfortable with agentic AI doing the job, according to February 2025 data from NLX and QuestionPro [6].

But even the most sophisticated agentic AI can unravel without the right checks in place. To keep things on track, decision-makers need a practical, repeatable checklist. Here are key questions every organization should be asking before putting AI agents to work:

• Are we testing for bias and explainability before deployment?

Don’t just assume fairness — prove it.

• What guardrails are in place to limit agent autonomy in high-risk contexts?

Define boundaries clearly to avoid unwanted surprises.

• Who signs off on agent roles and permissions?

Make ownership and accountability explicit.

• How are we ensuring data sovereignty and compliance?

From General Data Protection Regulation (GDPR) to local laws — know where your data lives and who can access it.

The Responsible Path to Agentic AI-Driven Transformation

AI is already reshaping our expectations: according to Salesforce [7], 44% of U.S. consumers would use an AI agent as a personal assistant, and 70% of Gen Z are eager to do so. From booking appointments (39% already comfortable) to avoiding repetitive conversations (34% would prefer agents over humans), people are ready for agentic AI that saves time and personalizes experiences. The appetite extends to shopping as well — 36% of consumers prefer digital or automated purchases, and 70% would use agents to optimize loyalty points.

To meet this momentum responsibly, organizations must address security and ethics from the start and take a proactive, interdisciplinary approach, uniting IT, legal, compliance, business, and innovation leaders to build with trust by design.

OneReach.ai’s Generative Studio X enables the design of agentic AI solutions that are not only intelligent but also secure, ethical, and people-centered. It empowers organizations to model, implement, operate, monitor, and optimize long-running processes with confidence.

Sources:
[1] PwC’s October 2024 Pulse Survey

[2] McKinsey’s report

[3] The Forrester report “Global Commercial AI Software Governance Market Forecast, 2024 to 2030”

[4] General Data Protection Regulation (GDPR)

[5] Organization for Economic Co-operation and Development (OECD) AI Principles

[6] February 2025 data from NLX and QuestionPro

[7] Salesforce statistics