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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →AI can help cybersecurity teams process data, filter alerts and prioritise vulnerabilities, but it can also help attackers find weaknesses and scale social engineering. IBM India & South Asia vice president for Technology Gaurav Agarwal’s central point is that faster automated security work needs firm access controls, accountability and human judgement—especially when a decision could affect an important business process.
How AI is changing cybersecurity
Security teams handle large volumes of data and alerts. Agarwal told Fortune India that AI can help process that information, filter alerts and prioritise vulnerabilities for action. The operational case is not that AI makes security decisions infallible; it is that it may help teams focus attention where it is most needed.
Agarwal argues that filtering can help teams manage alert volumes without reducing detection sensitivity. That is his assessment in the interview, not a measured performance result. Teams still need to check whether alerts are meaningful, decide how to respond and keep the underlying detection rules aligned with policy.
The same capabilities can be used against organisations. AI may help attackers identify vulnerabilities and scale social-engineering attempts. As a result, AI is not simply a defensive tool: it changes the speed and scale of work on both sides, while leaving organisations responsible for protecting systems and people.
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What the figures say—and what they do not
Fortune India reports several figures from named reports and surveys. They indicate that organisations see AI as a major cybersecurity issue, but they should be read with their source and attribution intact; the figures below are reported by Fortune India rather than independently verified here.
- The World Economic Forum’s Global Cybersecurity Outlook 2026 is cited as finding that 94% of respondents expected AI to be the biggest driver of change in cybersecurity in 2026, and 87% identified AI-related vulnerabilities as the fastest-growing cyber risk in 2025.
- The same WEF report is cited as finding that 54% of organisations considered insufficient knowledge or skills a major hurdle to deploying AI for cybersecurity.
- Fortune India reports that 77% of organisations had adopted AI for cybersecurity, but the page text does not identify the underlying survey publisher or report.
- IBM’s 2025 Cost of a Data Breach report is cited as putting the average breach cost in India at ₹220 million, up 13% from 2024. Fortune India also attributes to the report findings that 37% of organisations had AI access controls, nearly 60% lacked an AI governance policy or were still developing one, and shadow AI was associated with ₹17.9 million in additional average breach costs.
These figures describe reported expectations, adoption and costs; they do not establish that adopting AI by itself improves security or that a particular product will prevent a breach.
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Why human judgement still matters
AI outputs are probabilistic, not guaranteed answers. Agarwal put it this way: “AI is not a deterministic system. AI is a probabilistic system.” A system can surface a useful signal or recommendation, but people must judge whether it is sound, whether it complies with policy and what to do next.
In Fortune India’s account, Agarwal illustrates a possible division of work by saying agents could carry out eight steps of a ten-step process while a person participates in two to enforce policies and guardrails. This is an example from the interview, not a general rule or measured allocation. The important distinction is whether human review is placed at the point where a consequential decision is made—not merely whether a person is somewhere in the workflow.
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Agarwal’s related interview with The Indian Express calls for human involvement in critical decisions, particularly those affecting important business processes. The interviews do not set a universal threshold for which decisions must be escalated. Organisations need to define that threshold in their own policies, based on the impact of an action and the risks of getting it wrong.
How to secure AI agents and their access
AI agents introduce machine identities: software identities that may interact with systems and data. Each agent’s permissions and activity need to be managed, just as an organisation manages access for other identities. The Indian Express interview discusses several practical controls:
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- Apply least privilege. Give an agent only the data and permissions required for its assigned task, rather than broad access by default.
- Monitor activity continuously. Watch what agents access and do so unusual behaviour can be investigated.
- Sandbox agents where appropriate. Restrict the environment in which an agent operates, particularly when testing or handling untrusted material.
- Validate inputs. Check information supplied to an agent instead of assuming every input is safe or reliable.
- Keep audit logs. Record access and actions so an organisation can reconstruct what happened and review it later.
- Set human escalation points. Identify which actions require a person to review or approve them before they affect critical systems, data or business processes.
These are governance measures, not guarantees. Their usefulness depends on how narrowly permissions are scoped, whether monitoring and records are adequate, and whether escalation rules work in practice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance means knowing what the AI can access and why it acted
Controls need to answer basic accountability questions: who is responsible for an AI-assisted action, what information can the model or agent access, and can the organisation explain a decision afterward? Agarwal told Fortune India, “The business has to be accountable and responsible.” That responsibility does not transfer to an automated system because it performed part of the work.
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Traceability matters when a recommendation is challenged or an incident needs investigation. An organisation should be able to connect an action to the relevant identity, access rights, inputs and review process. Agarwal also described IBM watsonx.governance in the interview as a way to control model and agent use and provide traceability. This is an interview description, not an independent evaluation of the product.
What IBM’s examples cover
Fortune India names IBM Concert as an example for prioritising vulnerabilities and fixes, and watsonx.governance for controlling model and agent use and supporting traceability. Those mentions explain how Agarwal frames IBM’s enterprise software; the interviews do not compare vendors or establish that either product is more effective than alternatives.
Agarwal’s interview also gives estimates about how security work may be divided among autonomous, heavily AI-assisted and primarily human tasks, as well as an estimate of the current share performed by people. These are his views, not independently measured workforce statistics, so they should not be treated as a forecast organisations can apply directly.
A practical decision framework for organisations
Before allowing an AI system or agent to take action, an organisation can use the controls raised in the interviews as a checklist:
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- Define the task and boundary. Specify what the agent is allowed to do, which systems it can reach and which data it can use.
- Limit permissions. Grant only the access required for that task and make the responsible machine identity identifiable.
- Test safely. Use sandboxing where suitable and validate inputs before they can influence actions.
- Set review and escalation rules. Decide which actions may be automated and which require human approval, especially where business impact is high.
- Monitor and log. Track agent activity and preserve records that support investigation and accountability.
- Check explainability after the fact. Make sure the organisation can establish what data was accessed, what action was taken and who was responsible for oversight.
The interviews provide no universal formula for balancing automation and human review. The sound test is whether an organisation can constrain an agent’s access, detect and reconstruct its actions, and put an accountable person in the decision path when the consequences warrant it.
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