The Challenges of Agentic AI: Security Risks, Ethics, and Implementation Strategy
In Part 1 of this series, we explored agentic AI in cybersecurity and how it is transforming the industry operationally, from threat detection and SOC automation to vulnerability management and incident response. The promise is compelling: faster response times, reduced analyst fatigue, and security systems that can reason and act at machine speed.
But as agentic AI systems gain greater autonomy, they also introduce a new set of challenges that organisations cannot afford to ignore. Autonomous decision-making expands the attack surface, raises complex ethical questions, and demands a more mature approach to governance and implementation.
In Part 2, we examine the critical security risks, ethical dilemmas, and strategic considerations that leaders must address to ensure agentic AI becomes a force multiplier rather than a liability.
Security Risks Unique to Agentic AI
Agentic AI in cybersecurity systems differ fundamentally from traditional security tools. Their ability to learn, adapt, and act independently makes them powerful defenders, but also attractive targets for adversaries.
Adversarial Attacks
Adversarial attacks exploit the way AI models interpret data. By subtly manipulating inputs, attackers can cause AI agents to misclassify threats or take inappropriate actions. In a cybersecurity context, this could mean an AI system overlooking malicious traffic, incorrectly flagging legitimate activity, or even triggering defensive actions that disrupt business operations.
Because agentic AI systems often operate continuously and at scale, adversarial attacks can have outsized impact. A single successful manipulation may influence decisions across an entire environment before human operators realise something is wrong.
Data Poisoning
Agentic AI systems rely heavily on high-quality data to learn and improve over time. If attackers can inject corrupted or misleading data into training sets or live telemetry feeds, they can gradually skew the system’s behaviour.
This kind of attack is particularly dangerous because it’s subtle. Unlike a traditional breach, data poisoning may not trigger immediate alarms. Instead, it degrades the AI’s effectiveness slowly, leading to missed threats or flawed prioritisation weeks or months later.
Model Drift and Overconfidence
Even without malicious interference, AI models can drift as environments change. New applications, evolving user behaviour, and emerging attack techniques can all reduce model accuracy over time.
Agentic AI systems that operate with high autonomy can become overconfident in outdated assumptions if they aren’t continuously validated. Without strong monitoring and retraining processes, organisations risk trusting systems that are no longer aligned with reality.
The Risk of Over-Automation
Autonomy is one of agentic AI’s greatest strengths, but it also introduces systemic risk. When AI agents are authorised to take action without human approval, mistakes propagate at machine speed.
Blocking critical infrastructure, isolating the wrong systems, or failing to escalate a genuine breach can all have serious consequences. The challenge isn’t eliminating automation, it’s ensuring that autonomy is applied thoughtfully, with safeguards and escalation paths in place.
Ethical Dilemmas and Accountability
Beyond technical vulnerabilities, agentic AI forces organisations to grapple with ethical and governance questions that don’t have simple answers.
Accountability in Autonomous Decisions
When an AI system makes a security decision that causes harm, who is responsible? The organisation deploying the technology? The vendor that built it? The team that configured it?
As agentic AI becomes more autonomous, accountability becomes more diffuse. Regulators and boards, however, will still expect clear ownership. Organisations must define responsibility upfront, not after something goes wrong.
Transparency and Explainability
Many advanced AI systems operate as black boxes, making decisions that are difficult to interpret. In cybersecurity, this lack of transparency can undermine trust and complicate incident response.
Security leaders need to understand why an AI agent took a particular action, especially when responding to incidents, conducting audits, or demonstrating compliance. Explainability isn’t a “nice to have”, it’s essential for operational confidence and regulatory alignment.
Bias and Uneven Enforcement
AI systems learn from historical data, and historical data often contains bias. If left unchecked, agentic AI may apply security controls unevenly, over-monitor certain users or regions, or under-detect threats in others.
This is particularly concerning in global organisations where AI-driven decisions can affect employees, customers, and partners across jurisdictions. Ethical deployment requires continuous bias assessment and governance oversight.
Regulatory Compliance
As governments introduce AI-specific regulations alongside existing data protection laws, organisations must ensure their agentic AI deployments remain compliant. Autonomous systems do not reduce regulatory responsibility, they increase it.
From GDPR and UK GDPR to emerging AI governance frameworks, compliance must be designed into agentic systems from day one, not retrofitted later.
Implementing Agentic AI Safely and Strategically
To unlock the benefits of agentic AI while managing its risks, organisations need a deliberate and phased approach to implementation.
Assess Readiness Before Deployment
Agentic AI is not a shortcut around foundational security work. Organisations must first evaluate their data quality, infrastructure maturity, identity controls, and governance frameworks.
Poor data hygiene or fragmented tooling will undermine even the most advanced AI systems. Readiness assessments help ensure agentic AI is built on solid ground.
Start with Scoped Autonomy
Rather than granting full autonomy immediately, many organisations start by using agentic AI for decision support. AI agents can investigate alerts, correlate signals, and recommend actions while humans retain final authority.
Over time, as trust grows and performance is validated, autonomy can be expanded to lower-risk actions such as enrichment, triage, or containment in predefined scenarios.
Maintain Human-in-the-Loop Controls
Despite advances in automation, humans remain essential for judgment, ethics, and accountability. Human-in-the-loop models ensure that AI augments expertise rather than replacing it prematurely.
Clear escalation thresholds, override mechanisms, and review processes help balance speed with safety.
Continuous Monitoring and Governance
Agentic AI systems require ongoing oversight. Performance monitoring, drift detection, security audits, and ethical reviews should be continuous processes, not one-time exercises.
Strong governance ensures that systems evolve responsibly as threats, regulations, and business needs change.
The Future of Cybersecurity with Agentic AI
As agentic AI matures, it will reshape not only technology stacks but also cybersecurity roles. Analysts will spend less time chasing alerts and more time on strategy, threat modelling, and governance.
At a global level, agentic AI opens the door to new forms of collaboration. Autonomous agents could share threat intelligence in real time, coordinate responses across industries, and help organisations collectively defend against large-scale attacks.
But this future depends on trust. Trust in the technology, trust in the governance behind it, and trust that autonomy is being deployed responsibly.
Balancing Innovation with Responsibility
The promise of agentic AI in cybersecurity is real and transformative. Autonomous systems can dramatically improve speed, scale, and effectiveness in defending against modern threats. But without careful design and governance, they can also introduce new risks that are just as impactful as the problems they aim to solve.
Success with agentic AI requires a clear-eyed understanding of both its potential and its pitfalls. Organisations that approach deployment strategically will gain a lasting advantage. Those that rush adoption without safeguards may find themselves managing new kinds of incidents they’re not prepared for.
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