By Kriti Sharma, Senior Attorney – Regulatory Legal & Compliance (SEA), DPDPA & AI Governance Specialist
For years, artificial intelligence functioned as a prediction engine. It analysed data, produced answers and generated content, then waited for a human to act. Traditional AI followed rules, Machine Learning systems learned patterns and Generative AI created new content from those patterns. Agentic AI marks the next stage. It does more than respond. It takes action.
Agentic AI fetches information from enterprise systems, makes decisions in real time, executes tasks, triggers workflows and calls external tools. It behaves like a digital employee that can research, interpret, decide and act. This move from passive help to autonomous execution is why the productivity gains feel immediate and material.
It is also why the governance burden rises. Once AI touches live systems, moves data across borders, issues refunds, modifies IT settings or handles personal information, after the fact controls are too late. Enterprises are embracing Agentic AI because it removes bottlenecks and clears backlogs. Support teams resolve tickets faster. Finance teams validate invoices in minutes. Healthcare and insurance providers triage cases without delay. The value is real, but so is the new surface for risk.
Production grade autonomy introduces a simple truth. Agentic AI continuously interacts with sensitive data. It reads it, writes it into context, transforms it into prompts, calls tools with it and propagates it across systems. Without runtime controls that operate at the moment of action, the likelihood of exposure, financial error or regulatory violation increases. The question is not whether Agentic AI is useful. It is whether it can be deployed safely at scale.
Why Traditional Security No Longer Works
Enterprise security assumed data would remain inside the original system. Agentic AI breaks this assumption. The model actively pulls data into its working context. That context includes prompts that instruct the model, embeddings that store mathematical representations for retrieval, context windows that hold what the model reads at once and short term caches that orchestration layers use for speed. The model then pushes results back through APIs and tools that file tickets, issue refunds, query databases or change settings.
Once data travels through these layers, classic controls struggle to reverse exposure. A passport number inside a prompt cannot be unexposed by a later log review. A card number embedded and stored cannot be removed by blocking the network afterwards. Protection therefore moves to runtime, the exact moment the agent fetches data, reasons over it or initiates an action. Controls must operate there, not days later during an audit.
In practice this means redaction that cleans inputs before the model reads them, encryption that keeps every hop unreadable to outsiders, workflow governance that bounds tool use and approvals, and audit ready logging that proves what happened without storing raw identifiers. If you secure the moment when data moves, you avoid exposures that are otherwise impossible to unwind.
Where Risk Spikes in Real Deployments
Agentic AI already runs in customer support, finance, IT service management, healthcare and HR. It reads context, reasons and acts. That is where value comes from and where risk begins.
A support copilot drafts a reply quickly but can import addresses or PAN details unless inputs are cleaned. A finance agent prepares ERP postings but can pull bank numbers into prompts or emails unless those fields are tokenised and outbound messages checked. An IT agent triages incidents but verbose logs can expose credentials and unguarded runbooks can trigger mass restarts unless rate limits and approvals exist. In healthcare, an agent assembles prior authorisations but PHI can drift into embeddings unless detection and masking run inline with short memory. HR agents accelerate onboarding but role creep appears unless access is strictly purpose based.
The pattern is consistent. Keep the speed and the accuracy, but apply controls at the moment of action.
The Five Controls Every Enterprise Needs
- Data minimisation as a runtime discipline.
Give the agent only what the task requires. Filter fields before retrieval, enforce purpose based access, mask or tokenise high risk identifiers and keep working memory short lived. Treat national IDs and payment data with strict policies and keep them out of prompts, logs and embeddings. Expose less at the moment of action and you reduce risk before it exists.
- Encryption that covers every hop.
Workflows cross connectors, vector stores, caches, orchestrators and tool endpoints. Use strong encryption in transit and at rest, field level protection for high risk attributes and managed key rotation. Modern cryptography can protect every hop with minimal performance impact.
- Runtime redaction as the AI kill switch.
Route all traffic through a gateway in front of the model and tools. Inspect each request and response, detect high risk identifiers and replace them with masked or tokenised values. If the content is too sensitive or uncertain, pause the flow and send it for human review. This happens in milliseconds and is invisible to users. Redaction prevents data the model never needed from entering the workflow at all.
- Workflow level governance to control autonomy.
Define what the agent may do, what it must never do and when a human must approve. Run these checks in real time on every task. This keeps autonomy within a safe lane and prevents unauthorised actions or system changes.
- Audit ready logging that respects privacy.
Capture policy decisions, purpose tags, approvals and redaction events without storing raw identifiers. Use tamper protected, metadata first logs so you can investigate incidents and demonstrate compliance without creating new exposure.
From the DPO and CAIO desk: nine security guardrails that make scale safe
Data protection and privacy compliance.
Anonymise where possible, mask and encrypt by default and evidence compliance with the regimes that apply to your footprint. Treat all identifiers that can single out a person as high risk and keep them out of prompts, logs and embeddings unless strictly necessary.
Access control and human oversight.
Enforce role based access for everyone who touches the system and put human in the loop steps on actions with impact, such as refunds above a limit, privilege changes and cross border transfers.
Adversarial resilience.
Test against data poisoning, prompt injection and evasion. Build allow lists for tools and parameters and run red team exercises that try to force unsafe calls or contaminate embeddings.
Bias and fairness controls.
Measure and document fairness in sensitive domains. Define remediation steps and re test on new data to confirm improvement before release.
Monitoring and incident response.
Monitor for anomalies, bias and security events continuously. Maintain an AI specific incident plan with clear containment and notification paths.
Deployment and update governance.
Use isolated environments, version control and rollback for models, prompts and policies. Preview changes in a sandbox and promote only after checks pass.
Model lifecycle management.
Establish procedures for post deployment monitoring, periodic re evaluation and retirement. Tie retraining to drift indicators, performance thresholds and policy changes.
Autonomy boundaries for Agentic AI.
Set explicit limits on decision scope, tool usage and goal setting. Require escalation whenever the agent’s confidence is low or the impact crosses a threshold.
Fail safe and override mechanisms.
Provide a clear way to pause, throttle or disable the agent when systems misbehave, ethics are at risk or outcomes deviate from policy.
Practical challenges and how to solve them
Over redaction can reduce accuracy. Encryption and policy checks add latency. Agent identities become complex. Default memory can retain more than intended. Cross border routes can violate residency rules. Models can hallucinate. These are solvable. Tune redaction with confidence thresholds and human review on edge cases. Cache policy decisions and run checks asynchronously where possible. Give agents least privilege identities. Keep memory short lived. Use regional endpoints and enforce residency. Wrap tools with guardrails and simulate high impact actions before they run. Update governance as models and laws evolve.
Blueprint for responsible deployment
Start with an ingress gateway that detects personal data, applies runtime redaction and evaluates policies before the agent sees any content. Use a policy aware orchestrator to control which tools and actions are allowed. Encrypt everything in motion and at rest. Establish observability that tracks lineage, risks and anomalies. Insert human approvals where the stakes are high.
A 30 to 60 to 90 day path to production, from a DPO’s perspective
Days 0 to 30. Stabilise and contain risk, prove lawful and minimal processing. Create a register of agentic use cases with purpose, legal basis where applicable, data classes and countries touched. Map end to end data lineage. Switch on the ingress gateway with runtime redaction so government identifiers, payment details, health information and contact data do not enter prompts, embeddings, logs or tool calls. Disable long term memory and apply strict time limits on short term caches. Encrypt vector stores, caches and logs so accidental writes remain unreadable. Produce a short note documenting purpose limitation, minimisation and encryption scope.
Days 31 to 60. Govern and strengthen, embed accountability and oversight. Define the agent’s authorised actions and bind them to purpose based access and least privilege. Wrap each tool with guardrails that validate parameters and require human approval at risk thresholds. Turn on audit ready, metadata first logging that captures policy decisions, redaction events, purpose tags and approvals without raw identifiers. Review records of processing, risks, notices where needed and the path for data subject rights when an agent is in the loop.
Days 61 to 90. Test, validate and scale, demonstrate resilience and audit readiness. Run red team and tabletop exercises that inject identifiers, escalate permissions and attempt unsafe actions. Include cross border transfers, vendor exposure and embedding contamination. Launch controlled pilots in low or medium risk workflows with clear success measures for privacy and safety, including redaction accuracy, latency overhead from controls and any residual data in logs. Publish a short transparency note for stakeholders that explains purpose, safeguards and how to raise a concern or exercise rights.
The Bottom Line
Agentic AI closes service gaps by resolving routine tasks instantly, accelerates operations by automating multi step decisions and elevates customer experience with faster, context aware interactions. Autonomy must always be paired with oversight. AI should operate inside a governed, monitored and privacy aligned framework where controls activate at runtime. When runtime redaction, workflow governance, encryption and audit ready logging work together, organisations gain both safety and scale. With the right architecture, Agentic AI becomes powerful, secure and compliant. The enterprises that master this balance will define the next decade of digital innovation.