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A New Era of AI Security Begins
The cybersecurity world is entering a period of unprecedented transformation. As artificial intelligence rapidly evolves from simple assistants into autonomous agents capable of making decisions, executing tasks, coordinating actions, and interacting with other AI systems, traditional security models are struggling to keep pace.
Recognizing this growing challenge, the Open Worldwide Application Security Project (OWASP) has announced the creation of the Agentic Research Council, a major initiative designed to bridge the widening gap between cutting-edge AI capabilities and the slower-moving world of cybersecurity standards, governance, and research.
The official unveiling is scheduled for the OWASP GenAI Summit during Infosecurity Europe 2026, marking what many security experts consider one of the most important developments in AI security governance since the introduction of OWASP’s influential Top 10 guidance for Large Language Models.
Summary: Why OWASP Is Launching the Agentic Research Council
OWASP’s new Agentic Research Council was created in response to the explosive growth of agentic AI systems. These systems are fundamentally different from traditional software because they can independently plan, act, adapt, collaborate, and execute tasks at machine speed.
According to OWASP GenAI Security Project leadership, the council will unite researchers, universities, governments, security professionals, policymakers, and technology companies under a coordinated framework.
The objective is simple but ambitious:
Prioritize the most urgent AI security research.
Connect academic discoveries with real-world security needs.
Accelerate mitigation development.
Improve governance strategies.
Produce practical guidance faster than traditional standards organizations can.
Rather than allowing researchers and practitioners to work in separate silos, OWASP aims to create a continuous feedback loop where both communities strengthen each other.
Agentic AI Is Moving Faster Than Security Can Adapt
One of the biggest concerns highlighted by OWASP is the collapse of what security professionals traditionally call “time-to-impact.”
Historically, discovering a vulnerability, weaponizing it, and launching a large-scale attack required significant time, resources, and expertise. Agentic AI dramatically compresses that timeline.
An AI agent can identify weaknesses, build attack chains, generate exploits, coordinate multiple systems, and adapt its behavior almost instantly.
This shift challenges decades of cybersecurity assumptions.
Traditional security governance has largely focused on development-stage controls such as secure coding practices, architecture reviews, compliance audits, and vulnerability management. While these remain important, they may no longer be sufficient in a world where autonomous systems continuously evolve after deployment.
OWASP argues that security teams must increasingly focus on runtime visibility, behavioral monitoring, and real-time policy enforcement.
The Democratization of Agentic AI
A significant factor driving concern is the rapid democratization of advanced AI capabilities.
Open-source projects and locally deployable agentic frameworks have lowered barriers that once restricted advanced AI functionality to major technology companies.
As powerful models become more accessible, organizations of all sizes can benefit from AI innovation. However, the same accessibility also creates opportunities for malicious actors.
The cybersecurity community is now facing a future where highly capable autonomous systems are available not only to governments and corporations but potentially to virtually anyone with sufficient computing resources.
This widespread availability changes the threat landscape dramatically.
The challenge is no longer whether advanced AI will become commonplace.
The challenge is how quickly security defenses can adapt.
Building a Global AI Security Research Network
The Agentic Research Council aims to establish a structured global ecosystem for AI security research.
The council plans to:
Sponsor doctoral and academic research.
Create public research roadmaps.
Host working groups.
Coordinate security priorities.
Connect theoretical research with operational realities.
Feed findings directly into standards, tools, and guidance.
Transparency is expected to be a central principle.
OWASP leadership has emphasized that participation should remain open, community-driven, and globally collaborative.
This approach reflects
Multi-Agent Security Becomes Ground Zero
One of the first major research areas selected by the initiative is multi-agent security.
OWASP researchers recently published a preprint paper examining the risks that emerge when multiple AI agents interact with one another.
The concern is not simply that individual agents may be compromised.
The greater risk lies in emergent behavior.
When numerous agents communicate, exchange information, discover tools, and coordinate tasks, entirely new attack surfaces can appear.
These vulnerabilities may not exist when agents are evaluated individually.
As a result, traditional security assessments focused on isolated systems become increasingly ineffective.
Researchers argue that future security frameworks must evaluate the behavior of entire ecosystems of interacting agents rather than individual AI applications.
The Rise of Machine-Speed Cyber Warfare
Perhaps the most striking comparison made by OWASP experts is the analogy between agentic AI and military drone warfare.
Just as inexpensive drones transformed modern battlefields by enabling large-scale, coordinated operations at relatively low cost, agentic AI has the potential to reshape cybersecurity.
Instead of one sophisticated attacker operating a single campaign, organizations may face swarms of coordinated AI agents acting simultaneously.
These agents can:
Probe defenses.
Launch attacks.
Adapt tactics.
Share intelligence.
Recover from failures.
Continue operations autonomously.
The scale and speed of such activities could overwhelm traditional human-centered response processes.
A security analyst might investigate one alert at a time.
An AI swarm could generate thousands of meaningful events every minute.
Why Human-in-the-Loop May No Longer Be Enough
For years, AI governance frameworks have relied heavily on the concept of “human-in-the-loop.”
This model requires human approval before critical AI actions occur.
While effective for many applications, OWASP researchers warn that machine-speed environments may render this approach impractical.
If autonomous systems operate thousands of times faster than human decision-makers, requiring manual approval for every action introduces unacceptable delays.
Instead, experts increasingly advocate for a “human-on-the-loop” model.
Under this framework, humans supervise systems, define policies, monitor outcomes, and intervene when necessary while allowing automated controls to operate independently.
The shift reflects a broader realization that future cybersecurity defenses must increasingly rely on automation to counter automated threats.
New Governance Frameworks for Agentic AI
Alongside the council launch, OWASP is also releasing a new governance-focused publication titled The State of Agentic AI and Governance.
The report aims to provide practical guidance for organizations adopting AI technologies.
Among its core objectives are:
Risk classification frameworks.
Maturity assessment models.
Governance recommendations.
Runtime monitoring strategies.
Incident response integration.
Compliance mapping.
Importantly, the guidance is designed not only for future standards development but for immediate operational implementation.
Organizations deploying AI today require actionable controls now, not years later.
What This Means for Security Leaders
Security leaders can no longer view AI as merely another software component.
Agentic systems introduce entirely new operational realities.
CISOs, security architects, risk managers, and governance teams must begin preparing for environments where autonomous systems continuously interact, evolve, and make decisions at machine speed.
Visibility into agent behavior may become just as important as vulnerability management.
Behavioral analytics may become as critical as endpoint security.
Runtime governance may become as essential as secure development.
The organizations that adapt early will likely gain a significant defensive advantage.
What Undercode Say:
The launch of the Agentic Research Council represents far more than another OWASP working group.
It is an acknowledgment that cybersecurity has entered a fundamentally different era.
For nearly three decades, security programs have focused on protecting applications, networks, users, and infrastructure.
Agentic AI introduces a new category altogether: autonomous decision-making entities.
This changes threat modeling completely.
Traditional software executes predefined instructions.
Agentic systems create and adapt strategies dynamically.
Security teams are accustomed to protecting static environments.
Agent ecosystems are inherently dynamic.
The biggest challenge is not model security itself.
The biggest challenge is behavioral unpredictability.
Organizations currently measure risk through assets, vulnerabilities, and exposures.
Future risk models will increasingly need to account for autonomous interactions.
This creates a significant visibility problem.
Many enterprises already struggle to monitor human users effectively.
Monitoring thousands of autonomous digital workers will be exponentially more difficult.
Multi-agent environments also create attribution challenges.
Determining which agent triggered an event may become difficult.
Understanding why it occurred may be even harder.
Current SIEM and SOC workflows were not designed for autonomous ecosystems.
Future SOC platforms may require dedicated agent observability layers.
Runtime governance will likely become one of the fastest-growing segments of cybersecurity investment.
The
However, secure-by-design alone cannot predict emergent behavior.
Emergent behavior exists beyond design assumptions.
This is where
The
Research often arrives too late.
Operational teams often lack time for deep research.
Bringing both communities together increases collective response speed.
Another notable aspect is the democratization warning.
Historically, advanced cyber capabilities were concentrated among elite actors.
Agentic AI is rapidly distributing those capabilities.
This mirrors historical technology cycles.
Powerful tools become cheaper.
Cheaper tools become widespread.
Widespread tools transform entire industries.
The same pattern is unfolding in AI security.
The drone warfare comparison is especially compelling.
Small autonomous entities coordinating at scale can outperform larger centralized systems.
Cybersecurity may soon experience the same phenomenon.
The next generation of attacks may be less about sophistication and more about coordinated volume.
Thousands of inexpensive AI agents could become more dangerous than a single advanced threat actor.
Organizations that continue relying solely on manual review processes may struggle.
Automation will increasingly defend against automation.
The winners in this environment will be those who embrace visibility, runtime governance, behavioral analytics, and AI-native security architectures.
OWASP’s initiative arrives at precisely the moment the industry needs it most.
Deep Analysis: Security Operations in an Agentic AI World
As AI agents become operational entities, security teams will need new technical workflows and monitoring capabilities.
Runtime Process Monitoring
ps aux | grep agent systemctl status ai-agent journalctl -u ai-agent -f
Network Observation
ss -tulnp netstat -antp tcpdump -i eth0
Behavioral Analysis
auditctl -l
ausearch -k suspicious_activity
Containerized Agent Tracking
docker ps docker logs agent-container kubectl get pods kubectl logs agent-pod
System Resource Monitoring
htop vmstat 1 iostat -x
Threat Hunting Indicators
find /tmp -type f lsof -i grep "ERROR" /var/log/
Future AI security platforms will likely combine these traditional telemetry sources with:
Agent memory monitoring
Tool invocation tracking
Decision-chain auditing
Cross-agent communication inspection
Autonomous policy enforcement
Runtime trust scoring
Multi-agent swarm detection
Agent attribution systems
The transition from application security to agent security is already underway. The organizations building these capabilities today will define cybersecurity’s next decade.
✅ OWASP is launching the Agentic Research Council through its GenAI Security Project and Agentic Security Initiative.
✅ The initiative focuses on improving collaboration between researchers, practitioners, industry stakeholders, and policymakers in response to rapidly evolving agentic AI technologies.
✅ OWASP researchers have published work addressing multi-agent security challenges and are promoting runtime governance as a critical component of future AI security architectures.
Prediction
(+1) AI security research will become one of the fastest-growing cybersecurity disciplines over the next five years as enterprises deploy increasingly autonomous systems. 🚀
(+1) Runtime agent monitoring platforms will emerge as a major commercial market, comparable to the growth of endpoint detection and response solutions during the previous decade. 📈
(+1)
(-1) Organizations that delay adapting their governance frameworks may face significant visibility gaps when autonomous agents begin operating at scale. ⚠️
(-1) Multi-agent ecosystems could introduce entirely new attack categories that current security tools are unable to detect effectively. 🔍
(-1) Human-only approval workflows may become a bottleneck against machine-speed attacks, forcing enterprises to redesign security operations around automation. 🤖
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References:
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