The Challenges of Agentic AI: Security Risks, Ethics, and Implementation Strategy

Editor's Note: This is Part 2 of our comprehensive series on agentic AI in cybersecurity. If you haven't read Part 1, "Agentic AI in Cybersecurity: Applications, Benefits, and Real-World Impact," we recommend starting there to understand the foundational concepts and transformative benefits these systems offer. In this concluding post, we examine the critical challenges, risks, and strategic considerations that determine successful implementation.

In Part 1 of this series, we explored how agentic AI is revolutionizing cybersecurity through autonomous threat detection, intelligent SOC automation, and rapid incident response. We examined real-world implementations from Microsoft, IBM, Google, and others that are delivering measurable improvements in efficiency, detection capabilities, and response times.

However, as compelling as these benefits are, agentic AI's autonomous nature introduces unprecedented risks and complex ethical challenges that organizations cannot afford to overlook. Understanding these concerns—and how to address them—is critical for any organization considering deployment of these powerful systems.

Security Risks and Adversarial Capabilities

While agentic AI brings immense promise, it also creates new classes of cyber risk. As AI systems become more autonomous and embedded into core operations, the attack surface shifts — and in some cases, expands.

Adversarial Attacks

Adversarial machine learning involves techniques where attackers subtly manipulate inputs to deceive AI systems. For example, altering a few pixels in an image can cause an AI model to misclassify a malware file as benign. In cybersecurity, adversaries may craft network traffic or log patterns that intentionally evade AI detection or trigger false positives, leading to confusion and system overload.

MIT researchers have been at the forefront of studying these vulnerabilities. In a 2025 interview, MIT CSAIL Principal Research Scientist Una-May O'Reilly discussed developing agents that reveal AI models' security weaknesses before malicious actors can exploit them. These agents simulate adversarial behaviors to test and improve the robustness of AI systems against potential attacks.

Additionally, a 2025 working paper from MIT Sloan's Cybersecurity at MIT Sloan (CAMS) initiative highlights that adversarial attacks can deceive AI systems, leading to incorrect decisions or actions. The paper emphasizes the importance of understanding and mitigating these risks as AI becomes more integrated into critical systems.

These studies highlight the need for continuous evaluation and refinement of AI models to ensure their reliability and security in the face of adversarial threats.

Autonomy Exploitation

Autonomous agents, if hijacked, can become powerful weapons. A compromised AI system responsible for patch management, for example, could be manipulated to distribute malicious updates across an entire enterprise. The autonomy that makes agentic AI so effective also makes it dangerous if it falls into the wrong hands.

Recent studies have demonstrated that large language models (LLMs) can autonomously exploit real-world cybersecurity vulnerabilities. Researchers at the University of Illinois Urbana-Champaign (UIUC) demonstrated how large language model (LLM) agents can identify and exploit vulnerabilities without human intervention, raising concerns about the security of these advanced AI systems.

This makes securing the AI itself — including its access controls, update channels, and decision-making logic — a top priority.

Data Poisoning

Because agentic AI systems continuously learn from the data they ingest, they are susceptible to poisoning — where attackers feed them manipulated or toxic data to corrupt their learning process. Over time, this can lead to degraded detection accuracy, skewed priorities, or unpredictable behavior.

A notable example is the "ConfusedPilot" attack, identified by researchers at the University of Texas at Austin and Symmetry Systems. This attack targets Retrieval-Augmented Generation (RAG) AI systems by subtly altering training data, causing the AI to misclassify or misinterpret specific inputs without affecting its overall performance. Such targeted poisoning is particularly insidious because it can remain undetected while causing significant harm.

To mitigate these risks, organizations should implement robust data validation processes, monitor for anomalies in AI behavior, and ensure the integrity of data sources. Regular audits and the use of adversarial training techniques can also enhance the resilience of AI systems against data poisoning attacks.

Overreliance on AI

Perhaps the most dangerous risk is psychological: the false sense of security that comes from "outsourcing" vigilance to a machine. Overdependence on agentic systems can erode human oversight and reduce critical thinking, making organizations more vulnerable if the AI system malfunctions, is bypassed, or faces a novel threat it wasn't trained to recognize.

Cybersecurity should remain a human-machine collaboration. AI may be tireless and fast, but it lacks human context, creativity, and ethical reasoning — all essential during complex, ambiguous incidents.

Ethical Challenges and the Role of Human Judgment

The implementation of agentic AI in cybersecurity isn't just a technical challenge — it's an ethical one. These systems make autonomous decisions that can affect people's privacy, determine access to critical systems, and influence real-world outcomes for individuals and organizations. That's why ethical considerations must be built into every step of the deployment strategy.

Accountability

When an AI system makes a decision — say, to lock out a user or quarantine a system — who is responsible if that action turns out to be incorrect? Accountability in agentic AI is murky because actions stem from models that adapt autonomously over time. Unlike traditional tools where human actions are logged and traceable, agentic AI decisions may be the result of complex chains of logic with no obvious "owner."

This challenge is pressing enough that the European Union's AI Act — the first sweeping legislation of its kind — classifies cybersecurity-related AI systems as "high-risk." This designation requires organizations to implement strict documentation, human oversight, and risk management protocols. As a result, the regulation has slowed down AI adoption in the cybersecurity space, as companies must navigate added compliance hurdles — with steep fines for getting it wrong.

Transparency

Black-box AI systems — especially deep learning models — are notoriously difficult to interpret. Yet, transparency is critical in cybersecurity, where trust and auditability are non-negotiable. Security teams must be able to explain why a particular decision was made, especially when reporting to stakeholders, regulators, or incident investigators.

Tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are being adopted to help open the black box and provide explainability, but challenges remain in real-time environments where decisions must be made in milliseconds.

Bias and Fairness

If your agentic AI learns from biased data — say, a threat detection model trained only on North American enterprise logs — it may underperform or misclassify threats in other contexts. Worse, it could reinforce discrimination in access control, anomaly detection, or behavioral profiling.

This is especially sensitive in areas like insider threat detection or fraud prevention, where AI may flag behavior based on skewed historical data, leading to unfair scrutiny of specific user groups. Ethical deployment requires intentional bias mitigation strategies, including diverse training datasets and regular fairness audits.

Human Oversight

Ultimately, even the most advanced agentic AI systems benefit from strategic human oversight. These systems are built to operate autonomously — perceiving, reasoning, acting, and learning on their own — but that doesn't mean they should be left to run unchecked.

Just as autopilot in aviation handles complex tasks while a pilot remains on standby, agentic AI should be deployed within clearly defined guardrails. Human experts must remain in the loop at the right level — not micromanaging every action, but ensuring accountability, safety, and alignment with broader mission goals.

Policy, Governance, and Regulatory Considerations

As agentic AI becomes a central component of digital defense strategies, the policy landscape is evolving to manage its risks and align its use with broader societal values.

Data Protection Laws

AI systems ingest massive amounts of data — much of it sensitive. Compliance with data protection laws like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or the California Consumer Privacy Act (CCPA) is not optional. Organizations must ensure that agentic AI systems adhere to data minimization, lawful processing, and transparency standards.

Failing to do so doesn't just incur fines — it erodes customer trust and weakens the legitimacy of AI deployment in security operations.

AI-Specific Legislation

Governments are increasingly treating AI as a regulated technology domain. In addition to the EU Artificial Intelligence (AI) Act, countries like Canada, Singapore, and the U.S. are rolling out frameworks for ethical AI deployment that emphasize:

• Algorithmic transparency
• Human rights protection
• Safety and robustness testing

The National Institute of Standards and Technology (NIST) recently released its AI Risk Management Framework, offering guidelines for AI developers and deployers.

Industry Standards

Security standards are emerging from organizations like the International Organization for Standardization (ISO), Institute of Electrical and Electronics Engineers (IEEE), and the Cloud Security Alliance (CSA) to help ensure that agentic AI aligns with best practices. These cover areas such as model validation, ethical use, system auditability, and incident response readiness.

Collaborative initiatives like the Partnership on AI and the Global Partnership on AI are also working to develop cross-sector standards that prioritize human-centric design.

International Cooperation

Cyber threats don't respect borders — but that doesn't mean global AI governance should be uniform. While coordination is important for sharing threat intelligence and setting baseline norms, healthy competition between regulatory frameworks can actually drive better outcomes.

Jurisdictions experimenting with different approaches create opportunities to discover what works best in practice. Some will move faster or smarter than others — and that's a feature, not a bug. The goal isn't a single global framework, but a diverse regulatory landscape that balances innovation, accountability, and resilience.

Integrating Agentic AI into Organizations

Integrating agentic AI into your cybersecurity stack isn't a plug-and-play solution — it requires thoughtful orchestration across people, processes, and technology.

Assessment of Needs

Not every problem is a hammer looking for an AI nail — and that's especially true with agentic AI. Like any emerging technology, it should be deployed only after a careful gap analysis: Where are the actual pain points? Which tasks are bogged down by inefficiency, delay, or human error? Agentic AI can bring serious value in areas like incident response automation or threat intelligence enrichment — but only if the trade-offs make sense.

In heavily regulated environments, that means weighing the benefits of autonomy against the burden of compliance. Sometimes, the smartest move is to start small — with a tightly scoped pilot — and build from there.

Infrastructure Readiness

Agentic AI demands robust infrastructure — fast data pipelines, scalable compute power, and secure cloud environments. Many older on-premise systems are not AI-ready. Organizations may need to invest in upgrading their infrastructure or leveraging hybrid cloud-native security architecture.

Integration with existing Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), and Endpoint Detection and Response (EDR) tools is also essential to ensure AI agents have access to high-quality, real-time telemetry.

Talent Acquisition and Training

Cybersecurity teams must either upskill current staff in AI fundamentals or bring in new talent with experience in machine learning and data science. The intersection of cybersecurity and AI is a niche skill set, but demand is rapidly growing.

Additionally, business leaders, legal teams, and compliance officers should also receive foundational training on AI ethics and governance to ensure cross-functional alignment.

Continuous Monitoring and Evaluation

Once deployed, agentic AI systems must be monitored just like any other critical asset. Performance, drift, adversarial risk, and fairness should be evaluated routinely through scheduled audits and stress testing.

Organizations like Google and OpenAI have pioneered red-teaming for AI — an internal adversarial exercise to test AI resilience and ethical robustness.

Regulatory Compliance

For organizations deploying agentic AI, regulatory compliance isn't a box to check — it's a strategic imperative. Unlike more traditional automation tools, agentic systems introduce complex questions around accountability, transparency, and control. Which regulatory frameworks apply? What are the risks of non-compliance — including fines, reputational damage, or operational shutdowns? What are the ongoing costs of staying compliant, from audits to public reporting?

Organizations must not only understand the rules, but also assign clear ownership for managing them. As global AI regulations evolve, proactive compliance planning will separate the leaders from the liabilities.

The Future Role of Agentic AI in Cybersecurity

Agentic AI is not a short-term enhancement — it's the foundation of a future-forward cybersecurity ecosystem.

Proactive Defense Mechanisms

Rather than reacting to known threats, agentic AI will increasingly operate in "prevention mode," identifying weak points and deploying fixes before an adversary strikes. This anticipatory security model flips the defense paradigm on its head.

Integration with Other Technologies

The future of cybersecurity lies at the intersection of multiple frontier technologies. Agentic AI will work in tandem with:

• Blockchain: for secure, immutable identity and event logging.
• Quantum cryptography: to defend against quantum-enabled decryption attacks.
• Digital twins: for simulating breach scenarios in a sandboxed replica of enterprise environments.

This convergence will make security systems smarter, faster, and exponentially harder to crack.

Evolution of Cybersecurity Roles

As agentic AI handles detection and initial response, the role of human defenders will shift toward strategic planning, system design, and risk governance. Expect to see emerging titles like "AI Security Architect," "Ethical AI Officer," and "Cyber-Behavioral Analyst."

This transition parallels the industrial revolution in automation — freeing humans to focus on higher-order problems.

Global Security Collaboration

Real-time, AI-powered threat intelligence networks could allow defenders across industries and nations to crowdsource their response to emerging threats. Instead of isolated vendors detecting threats independently, agentic systems could instantly propagate threat signatures and mitigation protocols across entire ecosystems.

Think of it as herd immunity for cybersecurity — driven by machine collaboration but supervised by humans.

Conclusion

Agentic AI represents a significant advancement in cybersecurity, offering the potential for more efficient and proactive defense mechanisms. However, as we've explored throughout this two-part series, its integration must be approached thoughtfully, considering the associated risks and ethical implications.

In Part 1, we examined how agentic AI's autonomous capabilities are already transforming threat detection, SOC operations, vulnerability management, and incident response—delivering measurable benefits in efficiency, accuracy, and scale. These real-world implementations from industry leaders demonstrate the technology's transformative potential.

But as we've detailed in this second part, organizations shouldn't shy away from deploying agentic AI—they should design for it. That means embracing the autonomy these systems offer while building in the right layers of human oversight and accountability. The goal isn't to rein in the technology, but to ensure it operates in alignment with mission objectives, ethical standards, and regulatory expectations.

Key Takeaways from Our Series:

• Agentic AI's perceive-reason-act-learn loop enables truly autonomous security operations that scale beyond human capabilities
• Real-world implementations are already delivering dramatic improvements in response times, detection accuracy, and operational efficiency
• Critical risks including adversarial attacks, data poisoning, and overreliance require proactive mitigation strategies
• Ethical considerations around accountability, transparency, and bias must be built into deployment strategies from day one
• Successful implementation requires careful assessment, infrastructure investment, talent development, and continuous governance

The organizations that move thoughtfully but decisively will gain significant competitive advantages in an increasingly complex threat landscape. The question isn't whether agentic AI will become standard in cybersecurity—it's how quickly organizations can implement it safely and effectively.

For further insights into AI-powered cybersecurity solutions, consider exploring your options with a member of our team here at VikingCloud team.