Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Employees are adopting Artificial Intelligence (AI) tools to enhance their productivity, but they rarely consider the security implications of doing so. When an employee pastes sensitive customer data into an unapproved AI tool, that data is processed by a third-party model outside the organization’s control, often leaving no audit trail for security teams to review.

Zero trust is a cybersecurity framework built on the principle of “never trust, always verify,” meaning every user, device and session must be continuously verified for access to be granted and maintained. In federal environments, zero trust is especially critical because privileged accounts can provide access to sensitive systems, infrastructure and data.

Financial institutions rely heavily on third-party vendors like payment processors, banking platform providers and fintech integrations to maintain operational efficiency. In fact, according to Verizon’s 2025 Data Breach Investigations Report, 30% of data breaches involved a third party, including vendors with direct remote access to financial systems. As environments become more distributed and accommodate remote work, managing vendor access has become a modern security challenge.

Ransomware and stolen credentials are among the most common and harmful attack vectors targeting financial institutions. Since banking systems store valuable financial assets and sensitive customer data, organizations must demonstrate strict control and oversight of privileged access to support regulatory and audit expectations under frameworks such as SOX, PCI DSS and GLBA.

Although Jira serves as the system of record for many DevOps and IT teams, retrieving secrets or approving requests for privileged information often occurs on other platforms. Teams may depend on external tools, email messages or Slack chats to manage credentials or elevation requests, leading to context switching, audit gaps and delays that increase operational risk.

Investment firms operate at the heart of global capital markets, managing assets, executing large volumes of transactions and relying on technology to transfer funds in real time. For all of this activity, investment firms rely on trading platforms, which are systems that route orders to alternative markets, analyze data, execute trades and measure performance across portfolios.

Secure sharing in Keeper works by encrypting records with record-level encryption keys, enforcing granular permissions and giving administrators centralized policy control and audit visibility into how sensitive credentials, passkeys and privileged resources are accessed. Keeper’s zero-knowledge architecture ensures that only authorized users can decrypt shared data, while flexible sharing options support everything from everyday collaboration to enterprise-grade Privileged Access Management (PAM).

According to HIPAA Journal, phishing remains one of the most common and effective attack methods used against healthcare organizations and is a leading cause of healthcare data breaches. As healthcare becomes more digital, cybercriminals increasingly target clinicians and administrative staff to access Electronic Health Records (EHRs) and other Protected Health Information (PHI).

Non-human identities (NHIs) and AI Agents including service accounts, CI/CD credentials and cloud workload identities, now eclipse human identities in enterprise identity systems by 50:1 to 100:1. Modern identity security platforms must assign identities to these assets and furthermore, apply roles, access control policies, visibility and governance in order to secure the modern enterprise.

A useful way to evaluate a modern identity security platform is to look at three core pillars: strong authentication and access controls, Privileged Access Management (PAM) that reduces standing privilege and secure credentials and secrets management with continuous visibility. In this blog, we’ll break down what those pillars mean, how they work together and how to evaluate and roll them out without creating gaps or friction as you scale automation and agent-driven workflows.