AgenticAI systems are not simple tools — they are autonomous agents with
the ability to make decisions, take actions, and access sensitive
systems with limited human intervention. This brings unimaginable
power, but equally the same amount of risk.
Todeploy agentic AI responsibly, cybersecurity must begin before
deployment — starting with a Cybersecurity Maturity Assessment.
This is not a nicety. It’s a necessity.
Whydoes maturity come first?
Becauseagentic AI introduces a level of autonomy, speed, and decision-making
that outpaces traditional systems. These agents don’t just follow
rules; they interpret requests, access internal systems, and interact
with live data. If your infrastructure, access controls, monitoring,
and governance aren’t already mature, agentic AI will exploit those
gaps — whether by accident or by design.
Amaturity assessment gives organizations the ability to:
- Identify vulnerabilities that autonomous agents could manipulate
- Define access boundaries before deployment
- Implement monitoring and detection from day one
- Establish governance, policies, and escalation paths tailored to AI behavior
Onlywhen these foundations are in place can agentic AI be deployed safely
and responsibly.
Keysecurity areas that must be mature before deployment:
Identity& Access
Identityand access management (IAM) must be hardened. For example,
implementing frameworks such as Okta, Azure AD, or AWS IAM. Enforceleast-privilege and just-in-timeaccess through privileged access management (PAM) solutions like
CyberArk or HashiCorp Vault. Rotate credentials using automated
secrets management. By doing so, the system ensures agents operate
within tightly controlled boundaries, minimizing the risk of
privilege escalation or inappropriate access.
Monitoring& Observability
Real-timemonitoring, audit trails, and anomaly detection must be built in from
day one. If not done already, this can be accomplished by using SIEM
platforms like Splunk or Microsoft Sentinel, integrate agent
telemetry, and apply behavioral analytics to detect deviations. Thiswill allow for rapid detection and response to malicious or
unexpected agent behavior, maintaining transparency and
accountability.
DataSecurity
Oneway or the other, agents will interact with your sensitive data.
Define access boundaries, enforcecontext-aware controls,and apply data masking and lineage tracking using platforms like
Protegrity or Immuta. Bydoing so, organizations can ensure data privacy, compliance, and
integrity while minimizing risk of unauthorized disclosure.
Code& Model Risk
AI-generatedcode must be secured through an SDLC pipeline. Use tools like Snyk or
Checkmarx, and mandate human review before deploying agent-generated
scripts. Evaluatethird-party libraries regularly.Thisapproach helps prevent vulnerabilities from being introduced through
autonomous development and maintains code quality.
Governance& Oversight
Establishclear AI governance policies, escalation paths, and audit mechanisms.
Create an AI Risk Committee and embed human oversight for high-risk
operations. Thisensures organizational control over autonomous decisions and
compliance with regulatory and ethical standards.
Context-AwareControls
Implementadaptive access based on environment and agent behavior. Use
attribute-based access control (ABAC), dynamic policy engines, and
runtime telemetry to adjust permissions. Thisallows the system to respond intelligently to evolving threats and
reduces the blast radius of misbehavior or compromise.
Conclusion:
Beforeyou release agents in your environment, ask yourself: “Isour environment mature enough to trust a non-human actor with real
agency?”
Ifthe answer isn’t a confident yes, the next step is clear: Conduct a
Cybersecurity Maturity Assessment. Becauseonce the agents are running, it’s too late to redesign your
defenses. Agentic AI is shaping the future. Only mature environments
can shape it securely.
