Creator: Saugat Sindhu, World Head – Advisory Companies, Cybersecurity & Threat Companies, Wipro Restricted
October is Cybersecurity Consciousness Month, and this yr, one rising frontier calls for pressing consideration: Agentic AI.
India’s digital financial system is booming — from UPI funds to Aadhaar-enabled providers, from good manufacturing to AI-powered governance. However as synthetic intelligence evolves from passive massive language fashions (LLMs) into autonomous, decision-making brokers, the cyber menace panorama is shifting dramatically.
These agentic AI techniques can plan, cause, and act independently — interacting with different brokers, adapting to altering environments, and making selections with out direct human intervention. Whereas this autonomy can supercharge productiveness, it additionally opens the door to new, high-impact dangers that conventional safety frameworks aren’t constructed to deal with.
Listed below are the 10 most crucial cyber dangers of agentic AI — and the governance methods to maintain them in verify.
1. Reminiscence poisoning
Risk: Malicious or false knowledge is injected into an AI’s short- or long-term reminiscence, corrupting its context and altering selections.
Instance: An AI agent utilized by a financial institution falsely remembers {that a} mortgage is accepted resulting from a tampered document, leading to unauthorized fund disbursement.
Protection: Validate reminiscence content material commonly; isolate reminiscence periods for delicate duties; require sturdy authentication for reminiscence entry; deploy anomaly detection and reminiscence sanitization routines.
2. Software misuse
Risk: Attackers trick AI brokers into abusing built-in instruments (APIs, fee gateways, doc processors) through misleading prompts, resulting in hijacking.
Instance: An AI-powered HR chatbot is manipulated to ship confidential wage knowledge to an exterior e mail utilizing a cast request.
Protection: Implement strict device entry verification; monitor device utilization patterns in actual time; set operational boundaries for high-risk instruments; validate all agent directions earlier than execution.
3. Privilege compromise
Risk: Exploiting permission misconfigurations or dynamic function inheritance to carry out unauthorized actions.
Instance: An worker escalates privileges with an AI agent in a authorities portal to entry Aadhaar-linked data with out correct authorization.
Protection: Apply granular permission controls; validate entry dynamically; monitor function adjustments constantly; audit privilege operations completely.
4. Useful resource overload
Risk: Overwhelming an AI’s compute, reminiscence, or service capability to degrade efficiency or trigger failures — particularly harmful in mission-critical techniques like healthcare or transport.
Instance: Throughout competition season, an e-commerce AI agent will get flooded with hundreds of simultaneous fee requests, inflicting transaction failures.
Protection: Implement useful resource administration controls; use adaptive scaling and quotas; monitor system load in actual time; apply AI rate-limiting insurance policies.
5. Cascading hallucination assaults
Risk: AI-generated false however believable data spreads by way of techniques, disrupting selections — from monetary danger fashions to authorized doc technology.
Instance: An AI agent in a inventory buying and selling platform generates a deceptive market report, which is then utilized by different monetary techniques, amplifying the error.
Protection: Validate outputs with a number of trusted sources; apply behavioural constraints; use suggestions loops for corrections; require secondary validation earlier than crucial selections.
6. Intent breaking and objective manipulation
Risk: Attackers alter an AI’s targets or reasoning to redirect its actions.
Instance: A procurement AI in an organization is manipulated to at all times choose a specific vendor, bypassing aggressive bidding.
Protection: Validate planning processes; set boundaries for reflection and reasoning; defend objective alignment dynamically; audit AI behaviour for deviations.
7. Overwhelming human overseers
Risk: Flooding human reviewers with extreme AI output to use cognitive overload — a severe problem in high-volume sectors like banking, insurance coverage, and e-governance.
Instance: An insurance coverage firm’s AI agent sends lots of of declare alerts to workers, making it exhausting to identify real fraud instances.
Protection: Construct superior human-AI interplay frameworks; regulate oversight ranges primarily based on danger and confidence; use adaptive belief mechanisms.
8. Agent communication poisoning
Risk: Tampering with communication between AI brokers to unfold false knowledge or disrupt workflows — particularly dangerous in multi-agent techniques utilized in logistics or protection.
Instance: In a logistics firm, two AI brokers coordinating deliveries are fed false location knowledge, sending shipments to the fallacious metropolis.
Protection: Use cryptographic message authentication; implement communication validation insurance policies; monitor inter-agent interactions; require multi-agent consensus for crucial selections.
9. Rogue brokers in multi-agent techniques
Risk: Malicious or compromised AI brokers function exterior monitoring boundaries, executing unauthorized actions or stealing knowledge.
Instance: In a sensible manufacturing unit, a compromised AI agent begins shutting down machines unexpectedly, disrupting manufacturing.
Protection: Prohibit autonomy with coverage constraints; constantly monitor agent behaviour; host brokers in managed environments; conduct common AI crimson teaming workout routines.
10. Privateness breaches
Risk: Extreme entry to delicate consumer knowledge (emails, Aadhaar-linked providers, monetary accounts) will increase publicity danger if compromised.
Instance: An AI agent in a fintech app accesses customers’ PAN, Aadhaar, and financial institution particulars, risking publicity if compromised.
Protection: Outline clear knowledge utilization insurance policies; implement strong consent mechanisms; keep transparency in AI decision-making; permit consumer intervention to right errors.
This checklist will not be exhaustive — but it surely’s a powerful place to begin for securing the subsequent technology of AI. For India, the place digital public infrastructure and AI-driven innovation have gotten central to financial progress, agentic AI is each an enormous alternative and a possible legal responsibility.
Safety, privateness, and moral oversight should evolve as quick because the AI itself. The way forward for AI in India shall be outlined by the intelligence of our techniques — and by the energy and accountability with which we safe and deploy them.
