Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The next phase of Threat Exposure Management by Gayathri Kunapuli.

Article 32 of the General Data Protection Regulation (GDPR) does not specify which tools to use, however it requires organizations to implement "appropriate technical and organisational measures" to protect personal data, proportionate to the risk. What that standard’s vague wording demands in practice is where most compliance programs run into trouble.

Compliance teams know the pattern well: tracking down a missing access review sign-off at 11 p.m. the night before an audit, piecing together evidence from spreadsheets, email threads, and the gap between HR and IT. Access reviews keep appearing in SOC 2 exceptions, and the controls usually aren't the problem. The manual processes around them are. Many teams respond by buying a dedicated GRC (Governance, Risk, and Compliance) platform. Traditional GRC tools are structured repositories.

Agentic AI security protects autonomous AI systems that independently plan, reason, and execute multi-step actions across enterprise environments without continuous human oversight.

The NIS2 Directive is the European Union’s updated cybersecurity framework, designed to improve cyber resilience across critical sectors. Building on its predecessor, the Network and Information Systems (NIS) Directive, NIS2 significantly expands its scope to include industries such as healthcare, manufacturing, energy, transport and managed services. NIS2 also introduces stricter cybersecurity requirements, direct accountability for senior management and defined incident reporting timelines.

Information Technology (IT) security is the practice of protecting an organization’s systems, data and networks from unauthorized access and cyber threats. It encompasses a wide range of processes, policies and technologies designed to secure everything from employee devices to cloud infrastructure.

Insider threats often don’t trigger alerts because the activity relies on valid credentials, approved tools, and authorized workflows. When viewed as individual events, this behavior looks normal and stays below traditional rule thresholds. Risk accumulates across otherwise valid actions without producing a signal that meets alert thresholds.

AI agents don't behave like the playbooks security and IT teams have spent years building. They form intent, select tools at runtime, and chain actions across systems in sequences nobody pre-authored. This means dropping an LLM into an existing automation sequence and expecting it to act like a smarter playbook is the fastest route to ungoverned, unpredictable outcomes.

As a fintech organization, you depend on multiple systems like AWS, Databricks, Snowflake, Power BI, Stripe Treasury, Identity Providers (IdP), developer tools, internal operational platforms, and many more. Managing access and access level across platforms is often disconnected and spread across emails, Slack approvals, tickets, and sometimes spreadsheets. Obviously, this is inefficient. There'll be delays in onboarding. But that's the least of your worries.